Corpus-based Analyses of the Problem–Solution Pattern
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Corpus-based Analyses of the Problem–Solution Pattern
Studies in Corpus Linguistics (SCL) SCL focuses on the use of corpora throughout language study, the development of a quantitative approach to linguistics, the design and use of new tools for processing language texts, and the theoretical implications of a data-rich discipline.
General Editor
Consulting Editor
Elena Tognini-Bonelli
Wolfgang Teubert
The Tuscan Word Center/ The University of Siena
Advisory Board Michael Barlow
Graeme Kennedy
Douglas Biber
Geoffrey N. Leech
Marina Bondi
Anna Mauranen
Christopher S. Butler
Ute Römer
Sylviane Granger
Michaela Mahlberg
M.A.K. Halliday
Jan Svartvik
Susan Hunston
John M. Swales
Stig Johansson
Yang Huizhong
University of Auckland Northern Arizona University University of Modena and Reggio Emilia University of Wales, Swansea University of Louvain University of Sydney University of Birmingham Oslo University
Victoria University of Wellington University of Lancaster University of Helsinki University of Hannover University of Liverpool University of Lund University of Michigan Jiao Tong University, Shanghai
Volume 29 Corpus-based Analyses of the Problem–Solution Pattern. A phraseological approach by Lynne Flowerdew
Corpus-based Analyses of the Problem–Solution Pattern A phraseological approach Lynne Flowerdew Hong Kong University of Science & Technology
John Benjamins Publishing Company Amsterdam / Philadelphia
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The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984.
Library of Congress Cataloging-in-Publication Data Flowerdew, Lynne. Corpus-based analyses of the problem/solution pattern : a phraseological approach / Lynne Flowerdew. p. cm. (Studies in Corpus Linguistics, issn 1388-0373 ; v. 29) Includes bibliographical references and index. 1. Corpora (Linguistics) 2. Grammar, Comparative and general--Data processing. I. Title. P128.C68F56 2008 415'.0285--dc22 isbn 978 90 272 2303 6 (Hb; alk. paper)
2007031621
© 2008 – John Benjamins B.V. No part of this book may be reproduced in any form, by print, photoprint, microfilm, or any other means, without written permission from the publisher. John Benjamins Publishing Co. · P.O. Box 36224 · 1020 me Amsterdam · The Netherlands John Benjamins North America · P.O. Box 27519 · Philadelphia pa 19118-0519 · usa
For my father Albert Frederick Scovell, scientist and inventor
Table of contents
Acknowledgments chapter 1 Problem-Solution pattern: An overview and corpus analytic perspective Clause relations as a means of identifying the Problem-Solution pattern 1 Grammatical signals of clause relations for the Problem-Solution pattern 4 Lexical signals of clause relations for the Problem-Solution pattern 5 Corpus analysis of a grammatical signal for the Problem element 8 Corpus analysis of a lexical signal for the Problem element 10 Conclusion 11 chapter 2 Issues in corpus linguistics and discourse studies Methodologies 14 Contextual features 15 Interpretation of data 16 Corpus linguistics: Towards a multi-faceted approach 19 chapter 3 The two corpora: Context and compilation Contextual background of the Professional and Student corpus 21 Issues in corpus compilation 24 Conclusion 32 chapter 4 Frequency, key word and key-key word analysis of signals for the Problem-Solution pattern Classificatory framework for signals: Appraisal system 33 Frequency analysis of signals 35 Key word analysis of signals 39 Key-key word analysis of signals 44 Differences between PROFCORP and STUCORP 49 Conclusion 50
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viii Corpus-based Analyses of Problem-Solution Pattern
chapter 5 PROFCORP: Phraseological analysis of signals for the Problem element Classificatory framework: Causal semantic relations 53 Classificatory framework: Lexico-grammatical patterns 55 Analysis of problem and problems 57 Analysis of need 62 Analysis of impacts and impact 63 Conclusion 73 chapter 6 PROFCORP: Phraseological analysis of signals for the Solution element Classificatory framework: Functional categories for nominal signals 76 Classificatory framework: Grammatical / causal categories for adjectival and verbal groups 77 Analysis of recommendations 78 Analysis of solutions and solution 80 Analysis of recommended 82 Analysis of proposed 89 Analysis of implementation 92 Conclusion 94 chapter 7 STUCORP: Phraseological analysis of signals for the Problem element Analysis of problem and problems 98 Analysis of need 110 Conclusion 113 chapter 8 STUCORP: Phraseological analysis of signals for the Solution element Analysis of recommendations 115 Analysis of solutions and solution 117 Analysis of recommended 120 Analysis of proposed 123 Analysis of implementation 126 Conclusion 128
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chapter 9 General conclusions and implications for pedagogy Some principal findings from PROFCORP 129 Expert vs. apprentice writing 131 Pedagogic implications and applications of findings 133 Overall conclusions 138 Appendices References Name index Subject index
Table of contents
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141 165 175 177
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Acknowledgments
I am greatly indebted to Michael Hoey for his invaluable guidance, inspiration and encouragement in carrying out the research for this book. I am also grateful to Tony McEnery and Mike Scott for their insightful comments on an earlier draft. My thanks go to the anonymous reviewer and Elena Tognini-Bonelli, the series editor, for all their advice. I would also like to thank Ulla Connor for her support and encouragement for my work over the years. Colleagues, Pansy Lam, Edward Li, Jacqui Lam McArthur and John Milton, have provided friendship, conversations and moral support over the past 15 years, for which I am very grateful. Last, but not least, I wish to thank my husband John and my sons, Rupert and Humphrey, without whose constant support, encouragement and understanding this book might never have been written.
chapter 1
Problem-Solution pattern An overview and corpus analytic perspective
One of the most common patterns of text organization is the Problem-Solution pattern, comprising four main elements: Situation, Problem, Solution and Evaluation. This pattern functions as the main organizing principle of many different kinds of written and spoken texts ranging from advertisements to workplace reports and has been extensively studied by Hoey (1983, 1986, 2001) and Jordan (1984) among others. An annotated bibliography of the early work on the Problem-Solution pattern by linguists such as Beardsley, Becker, Labov and Winter can be found in Hoey (1983: 189–201). Much of the discussion and analysis of this pattern has focused on clause relations as a means of identifying the pattern, and also on the grammatical and lexical signals for realizing the basic elements of the pattern. This introductory chapter illustrates these key concepts and concludes by making a case for identification of the signals for the Problem-Solution pattern using corpus analytic techniques.
Clause relations as a means of identifying the Problem-Solution pattern Hoey and Winter’s (1986) starting point for analysis of the Problem-Solution pattern is with how discourse is created through clause relations, then moving on to the ways in which these clause relations are signaled. Moreover, both Winter and Hoey stress that a clause relation is a cognitive process whereby the reader interprets the discourse in a particular way set up by inferential connections made by the writer. Besides the interpretative nature of clause relations, another observation is that the clause relation does not relate only to clauses or adjacent sentences, but can also refer to the relation between two paragraphs, which can be seen as a larger clause relation (Hoey 1983). This aspect is important in that it recognises that the Problem-Solution pattern is not confined to the level of the clause, sentence or paragraph (as was initially thought by Becker 1965), but can refer to any unit of discourse above the level of the clause. The observation that different elements are
Corpus-based Analyses of Problem-Solution Pattern
not necessarily co-terminous with paragraphs, sentences or clauses can be illustrated by the following example for an Internet service from Hoey (2001: 128): TRYING TO WORK WITH THE INTERNET? IS THE INTERNET TURNING YOU INTO A MONSTER? LET MCIS HELP YOU TO CONTROL THE BEAST. MCIS is a Total Internet Solution Provider and can assist you in the following areas: [A list follows]
The Situation element in the first sentence and the Problem element in the second sentence are both co-terminous with their respective sentences. However, the third sentence offers a Solution (MCIS) as well as a positive evaluation (help) and reiterates the problem (beast), signaled by the near-synonym monster in the previous sentence. In the above example, the evaluative element is embedded in the Solution and both the Problem and Solution elements extend across clauses and sentences. This nature of textual patterning has been commented on by other discourse analysts, most notably McCarthy (1991): These patterns are manifested in regularly occurring functional relationships between bits of text. These bits may be phrases, clauses, sentences or groups of sentences; we shall refer to them as textual segments to avoid confusion with grammatical elements and syntactic relations within clauses and sentences. A segment may sometimes be a clause, sometimes a sentence, sometimes a whole paragraph; what is important is that segments can be isolated using a set of labels covering a finite set of functional relations that can occur between any two bits of text. (McCarthy 1991: 28)
‘These functional relationships between bits of text’ referred to by McCarthy above are synonymous with the types of clause relations summarised in Hoey (2001: 30), namely Sequence relations (e.g. time, cause-consequence, means-purpose, and premise-deduction) and Matching relations, which include contrast, similarity, exemplification, preview-detail and exception. These clause relations can themselves act as signals of Problem-Solution patterns because these signalling relationships regularly co-occur. With specific reference to the Problem-Solution pattern, Hoey notes that ‘… the relation between Problem and Response is also one of Cause–Consequence and that between Response and Result is also one of Instrument–Achievement’. (Hoey uses the term ‘Response’ rather than Solution when referring to this individual part of the pattern, and employs the term ‘Result’ when a successful outcome to the Solution is achieved). However, it should be noted that evidence of the existence of the cause-consequence relation
Chapter 1. Problem-Solution pattern
does not necessary entail evidence of the existence of the Problem-Solution pattern (Hoey 1983). By way of illustration, in an excerpt from the discussion section of a finalyear undergraduate engineering project report in Figure 1-1, in the Problem 1b + Solution pair, the cause is signalled by However, in the first sentence and the consequence by As a result, in the second sentence. In this example, there is also an Instrument–Achievement pair, where the main clause in the second sentence (…we added an air pump…) signals the Instrument, and the subordinate clause (…allowing external air …) the Achievement. Results Analysis Modifications Although we could not test the concentration of oxygen in the seawater due to equipment failure we could observe that the fish in the tank lacked oxygen as most of them came up to the water surface for respiration. The original air injection system integrated with the filter could not provide enough oxygen to the culture. We added an external air pump to improve the situation. However, we could not inject air into the tank directly as foam might form. As a result, we added an air pump into the foam removal unit, allowing external air to be injected into the unit.
Situation Problem 1a + partial Solution Problem 1b + Solution
In order to remove carbon dioxide from the culture, we put some seaweed Problem 2 + in the tank. This is the most efficient way to remove carbon dioxide from Solution + the water. Evaluation
Figure 1-1. Example of clause relations in the Problem-Solution pattern (Flowerdew 2003: 491)
In fact, the above extract in Figure 1-1 is a modification of the pattern, in this case ‘progressive multilayering’, where each Solution only solves part of the Problem (see Hoey 1983: 81–106 for variations of the basic pattern). The following section examines clause relations in more detail to determine how the clause relations (and hence the Problem-Solution pattern) are signalled grammatically and lexically to the reader. Although the means of signalling clause relations for the Problem-Solution pattern have been discussed in the literature under the categories of elicitation techniques (i.e. questioning and paraphrasing), grammatical signals, lexical signals and lexical repetitions, I shall confine my discussion to grammatical and lexical signals as these are the foci of the computational analysis in this book.
Corpus-based Analyses of Problem-Solution Pattern
Grammatical signals of clause relations for the Problem-Solution pattern The earliest work in this area was carried out by Winter (1971, 1977) who illustrates how certain closed-set grammatical items such as subordinators and sentence connectors (comprising adjuncts) act as signalling devices for the Problem-Solution pattern. A list of the subordinators and sentence connectors, which he terms Vocabulary 1 and Vocabulary 2 items respectively, is given in Winter (1977). I will now examine some examples from the literature where the logical sequence of Instrument–Achievement clause relations, which as stated previously can signal Response and Result, can itself be signalled by these finite categories of grammatical connectives. One key aspect to note about these Vocabulary 1 and 2 items is their interchangeability, in certain circumstances, not only within a vocabulary type but also across vocabulary types. Winter (1971: 45) cites the following example of an Instrument-Achievement relation to show the syntactic and semantic properties of so. In the sentences below, so can be replaced by thus, another grammatical item from the same Vocabulary 2 class. However, one of the questions that still needs an answer is under what circumstances we would use one signal rather than another given their apparent changeability. (3) a. The hovercraft terminals can be sited away from the main ports, and so relieve overcrowded dock systems. (3) b. The hovercraft terminals can be sited away from the main ports, thus relieving the overcrowded dock systems.
Other examples of the Instrument–Achievement relation taken from Proctor (1988: 25) demonstrate how a Vocabulary 1 item, the subordinator by -ing, can be substituted by the Vocabulary 2 sentence connector Thus. By appealing to my father’s sense of humour, I avoided upsetting him immediately when I told him that his car had been stolen outside the police station. I appealed to my father’s sense of humour. I thus avoided upsetting him immediately when I told him that his car had been stolen outside the police station.
However, replacement of one item with another is not always possible, as the choice of one over the other is governed by the context. As Proctor (1988) points out the grammar of subordination in the first sentence above presents the information of its clause as given by the context of the utterance, whereas the grammar in the second sentence presents the same information as new. Grammatical choices are therefore highly dependent upon not only the semantic relations ex-
Chapter 1. Problem-Solution pattern
isting between clauses and sentences, but also pragmatic factors derived from the context. So far, these grammatical items have been discussed in terms of their signalling effectiveness for identifying clause relations, but as Hoey (1983) points out our starting point can also be with a description of clause relations as a way of shedding light on the nature of these devices. He also notes that for Winter the signal and relation are of equal importance, with each requiring a description of the other for identification. Another important point to note is that although these clause relations tend to be realised by certain grammatical items, by no means is there a one-to-one correspondence between the signal and its clause relation: ‘Texts often contain strong clues or signals as to how we should interpret the relations between segments; these are not absolutely deterministic but are supporting evidence to the cognitive activity of deducing relations’ (McCarthy 1991: 29). However, attempts have been made to provide lists of grammatical items as ‘supporting evidence’ for identifying the Problem-Solution pattern by Jordan (1984) and Proctor (1988). Based on her example texts, Proctor, conflating Winter’s Vocabulary 1 and 2 items, gives a list of grammatical exponents for realising each of the four basic components of the Problem-Solution pattern. Jordan’s lists are somewhat different from those of Proctor as he does not discuss Winter’s vocabulary 1 and 2 items, but classes both grammatical and lexical items under a category of Signals of Logic. Here, some of the grammatical items such as by …ing and so belong in Winter’s Vocabulary 1 items of subordinators, whereas others such as as a result and therefore belong to his Vocabulary 2 items of sentence connectors. Although the classification lists of Jordan and Proctor are not without their respective merits, an inherent weakness with both of them is that they do not consider the mediating role of clause relations in the process: ‘… supplying connections to a discourse with subordination and conjuncts is a test not of the existence of the Problem-Solution pattern but of the existence of particular relationships (i.e. Cause–Consequence, Instrument–Achievement) holding between (normally) adjacent parts of a discourse’ (Hoey 1983: 57).
Lexical signals of clause relations for the Problem-Solution pattern The picture is a little clearer for those lexical signals of the Problem-Solution pattern as there exist more areas of agreement among researchers as to what constitutes lexical signals. Although Hoey (1983) mentions that lexical signalling can take the form of a sentence, clause or phrase, the normal procedure is to focus on individual lexical items, which is the case in this section. Hoey’s definition (1983: 63) emphasises the importance of their role in the encoding/decoding of
Corpus-based Analyses of Problem-Solution Pattern
textual meaning, thus underscoring the intentional and interpretative nature of such signals: ‘Lexical signals are the author’s/speaker’s explicit signalling of the intended organisation and are therefore obviously of primary importance; it is probable that they are one of the main means whereby a reader/listener ‘decodes’ a discourse correctly’. Jordan (1984: 4–5), meanwhile, suggests specific lexis for signalling the Problem-Solution pattern: Within a defined situation, you will recognise a ‘problem’ in the widest sense of the word. …words that indicate this concept – not just the word problem itself, but its near-synonyms difficulty, dilemma, drawback, danger, snag, hazard, and so on, and words such as pest, unpleasant, disorganised, fear, smelly and illness. Whenever we recognise such a word in the text, we expect the text to tell us of a solution (actual, attempted, or proposed), and solutions are recognised as things or actions that avoid, counteract, reduce, prevent or overcome the problem. Then the text may evaluate the effectiveness of the solution with such words as excellent, important, quick, unique and failure.
Lexical signals for the Problem-Solution pattern have been discussed by Winter (1977) under the rubric of ‘Vocabulary 3’ items. These are discourse-organising words which can also replace Vocabulary 1 or 2 items, outlined in the previous section, to express the same meaning. To take an example from McCarthy (1991: 29), the Cause–Consequence relationship can be expressed through the Vocabulary 3 item reason, e.g. ‘The reason he went home was that he was feeling ill’ as well as through the Vocabulary 1 item because as in the sentence ‘Because he felt ill, he went home’. There therefore exists a choice between a lexical (i.e. Vocabulary 3) or a grammatical item, Vocabulary 1 in the case above, just as there exists a choice between different grammatical items within Vocabulary 1, as mentioned previously. However, under what conditions one grammatical item would be preferred over another, or a lexical item preferred over a grammatical item to convey the same clause relation, is obviously dependent on certain pragmatic and contextual features of the discourse. To illustrate how these various Vocabulary 3 items operate in text as signals for the Problem-Solution pattern, let us examine the following example from Harris (1986: 163). S3 On October 9th Henry set off for Calais, leaving half of his arms at Harfleur and taking the other half with him. S4 It had been raining heavily in the last few days and all the rivers were swollen. S5 Henry found it very difficult to cross the fords and rivers as the French army always ran parallel and protected each fording place.
Chapter 1. Problem-Solution pattern
S6 Henry solved this problem by cutting very quickly across a neck of the land before the French could and he managed to get across. (H.2.A.11)
In the above example, the lexis difficult, solved this problem and managed all function as signals for various elements of the Problem-Solution pattern, but act as signals in different ways. Solved and problem clearly have a discourse-organising role: the item this problem refers retrospectively to the fact that it was ‘very difficult to cross the fords and rivers’ and solved sets up an anticipated solution. However, it should be pointed out that whether a noun such as problem functions anaphorically is dependent on its accompanying deictic. In the phrase This problem, it is the demonstrative This which carries the burden of anaphoric reference. Here, problem has the function of what is being referred to. The items difficult and managed, while not signalling the overall text organisation, still operate as lexical signals for Problem and Evaluation respectively, acting as the referential vocabulary for these elements, and thus play a more local role in creating textual coherence. Obviously, the same lexical item can operate either as a referring (discourse-organising) or referential (discourse) signal depending on other contextual features of the discourse. For instance, in the example supplied above, in S5 we could paraphrase ‘… very difficult to cross the fords and rivers’ as ‘a problem to cross the fords and rivers’. In this case, the item problem would be acting as a local discourse signal rather than a connective one, binding adjacent clauses and sentences, as in S6 above. It is also worthwhile to mention here the other terms used in the literature, besides Vocabulary 3 items, to designate those types of nouns which have a metadiscursive i.e. discourse-organising function and rely on the context for their full interpretation. Francis (1986, 1994) refers to ‘anaphoric nouns’, Ivanič (1991) talks of ‘carrier nouns’ and Schmidt (2000) of ‘shell nouns’ – see Schmidt (2000, Chapter 2) for a helpful review of these overlapping categories. More recently, Flowerdew’s (2003a, 2003b, 2006) corpus-based research on signalling nouns reveals the key discourse role such types of abstract nouns play in establishing links across and within clauses. As regards the Problem-Solution pattern, both Jordan and Proctor have supplied useful sets of lexis realizing different elements of the pattern; however, the drawback of both of these lists is that they are based on a limited number of texts. Proctor’s analysis is based on only four academic texts in the fields of Science and Technology while Jordan’s list is derived from a somewhat random choice of various text segments covering different genres and registers. Proctor, writing presciently in 1988, notes that such analysis for the identification of lexical signals could very usefully be aided by computational techniques:
Corpus-based Analyses of Problem-Solution Pattern
The work of compiling an index of discourse signals that could eventually be incorporated in the contextual grammar of English, though lengthy and timeconsuming, is not impossible. Recent advances in information technology have greatly facilitated statistical counts and storage. It is possible that certain word or phrase locating programs can be used to speed up parts of the analysis. Indeed, the development of computational techniques for this kind of analysis may present challenging and rewarding lines of enquiry for interested individuals. (Proctor 1988: 42)
The following two sections give a taste of how a grammatical and lexical signal for the Problem element can be fruitfully analysed from a corpus analytic perspective based on the phraseological approach to language.
Corpus analysis of a grammatical signal for the Problem element One key grammatical item that has been frequently mentioned as a signal for the Problem-Solution pattern is the connector however. This item was searched in a corpus of professional environmental reports (PROFCORP) of approximately 225,000 words comprising 60 executive summaries, one of the two specialized corpora under discussion in this book (see Chapter 3 for a description of this corpus). Out of a total of 8,724 types (the number of different word forms), however was found to be the 100th most frequent with 264 tokens. In spite of its high frequency, it did not show up as a key word, i.e. a word of unusually high frequency when compared with a large-scale general reference corpus (Scott 1997, 2001a) in this case, the 100-million word British National Corpus, BNC. This may well be because however is used not only in the technical genre of report writing but also in everyday English as providing evidence for the Problem-Solution pattern. It would be of interest to examine how this item functions from a phraseological perspective, i.e. to have a look at its colligational and lexico-grammatical patterning and how it relates to different elements of the Problem-Solution pattern. Colligation, a phenomenon first described by Firth (1957), refers to ‘the grammatical company a word keeps’ (Hoey 1997: 8). For example, Hoey (1993), using a corpus of just under 100 million words, demonstrates how reason has a colligational relationship only with the demonstrative deictics and not with the possessive ones. Colligation also refers to the positioning of a word in a sentence, another concept which has been variously defined. Francis (1991) takes this to mean the distribution of a word across subject, object and complement slots in a sentence, whereas Hoey also considers this term from the Hallidayan perspective of Theme / Rheme position. In my specialized corpus, however was found to have
Chapter 1. Problem-Solution pattern
a colligational preference for sentence-initial position with 184, i.e. 70% of the tokens occurring in this position. As expected, most of the instances of however introduced problems that might or might not arise from the proposed construction activities, with reasons given. However, water quality impacts may arise due to contaminated runoff from the construction sites. However, as the proposed road improvement scheme is well away from the sea, there would not be any direct discharge of effluent to sea waters.
Three other patterns for however were also discernable. First, it was used to indicate that the solution was only a partial one, and that an aspect of the problem still remained (an example of ‘multilayering’), as in Table 1-1 below. In the other two patterns however was used as a linking device, binding the Problem and Solution elements. The structure in Table 1-3 (however, with + nominalization) was found to occur in concluding-type sentences where the proposed solution had already been discussed earlier in the report.
Table 1-1. Concordance for however to indicate a partial solution Site were used to accommodate the car park. ul design of flood lighting to minimise glare. ng Lap Kok eastern shore will remain intact. ecked over so that the noise will be enclosed.
However However However, However,
these are insufficient to allow full co the effectiveness of these measur the revised configuration will also these new roads will attract addit
Table 1-2. Concordance for however to signal solution to problem or disposal to a non-containment landfill site. e of the acceptable noise levels are exceeded. ishing activities in the Western harbour will, ting vegetation will result in visual intrusion ondary schools near the road. The impact can
However, However however, however, however
filtered dust could be landfilled at S 3 dBA should be added to the pre be progressively curtailed in the a these slopes will be planted and the d be minimized by appropriate mitigati
Table 1-3. Concordance for however to signal solution to problem mended TSP hourly guideline of 500 ug/m3. in noise levels similar to ambient conditions. itive receptors at the Lung Kwu Tan villages. concentrations may exceed acceptable limits. suspended particles lost from surface soils.
However, However, However, However, However,
with the implementation of standard with the provision of suitable site with the provision of appropriate with the adoption of dust supression with the use of hard surfaces and an
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Corpus-based Analyses of Problem-Solution Pattern
Corpus analysis of a lexical signal for the Problem element Pollution was found to occur 220 times in PROFCORP. Although this item was less frequent than however (264 occurrences), it was found to be key in the corpus as a whole, whereas however was not. When each individual report was compared with the larger-scale reference corpus, pollution was also found to be key in 10 out of the 60 reports (see Flowerdew 2003 for further details on keyword analysis). These results show that the keyness of a word may not necessarily be related to frequency, in cases where the word reflects the topic of a specialized genre. Pollution was also found to have certain collocational preferences and patterning. It should be noted that, like colligation, collocation has been variously defined. Whereas Sinclair (1987) and McEnery and Wilson (2001) relate collocation to measures of statistical significance, i.e. considering lexical items with items that appear with greater than random frequency, Cowie and Howarth’s (1996) approach is to favour the “textual” over the “statistical” identification of collocates for the following reasons: Collocations are often described as fixed and recurrent word-combinations…. But both parts of this description are misleading. Typically, collocations are not fixed but variable to a limited and arbitrary degree. As for frequency, it can be shown that individual restricted collocations may recur to only a limited extent within a given text or across several texts devoted to the same topic [my italics]. It is best to think of a collocation as a familiar (institutionalized), stored (memorized) word-combination with limited and arbitrary variation. (Cowie & Howarth 1996: 82)
Likewise, Stubbs (2001c: 74–75) puts forward a similar reason as to why measures of statistical significance may be of limited use in some cases. He cites the example of a small corpus yielding the following data for the node adverb ‘distinctly’: – Stubbs remarks that the above adjectival collocates occurred only once each and therefore statistical measures to determine the likelihood of co-occurrence could not be carried out. What he does pinpoint in these data, is the attraction of ‘distinctly’ with disapproving words, thus emphasizing the interpretation of corpus findings by the human analyst. This is the approach I have tended towards in this book – interpretation of small corpus data by the human analyst, not only interpretation of the text internally at the lexico-grammatical level, but also externally with recourse to contextual and situational features of the discourse.
Chapter 1. Problem-Solution pattern
Table 1-4. Concordance of pollution followed by ‘from’ ll be dust emissions from site formations. Air Similarly means to reduce the potential for the use of silt/oil traps will prevent marine llowing development may result from traffic pended solid matter in site run-off or organic
pollution pollution pollution pollution pollution
from site and motor vehicles are likel from fuel spillage on site have be sug from on-site construction activities at from the new road network. Pollutan from foul effluent. As the scale of th
Table 1-5. Concordance of pollution in means-purpose clauses tion phases of the development. To minimize trmwater. All possible measures to minimise Works to the Urmston Road; to minimise outside the embayment area to reduce the the south of the western seawall. To prevent
pollution pollution pollution, pollution. pollution
and nuisance from the development loads should be implemented and environmental and ecological disturb Loading into the trapped body of wat of marine waters by floating debris
To return to the analysis of problem, I examine its collocations from a “textual” perspective as certain collocations would show up ‘to only a limited extent’ in this specialized corpus. As for the collocational preferences of pollution, air and water were the most common, co-occurring 39 and 27 times with pollution, respectively. Pollution also occurred 14 times in what appeared to be a semi-fixed phrase allowing some lexical variation: environmental protection and pollution control measures / requirements. Pollution typically occurred in two main phraseologies. In the first pattern pollution was followed by from, a reduced form of arising from which was sometimes used instead, and thus involved a cause-consequence relationship as shown by the examples in Table 1-4. Pollution was also found in means-purpose clauses, with ‘two-way’ signaling verbs, such as ‘reduce’, ‘prevent’ and, in particular, ‘minimize’. Such verbs are another means whereby the Problem and Solution elements are linked (Table 1-5). The sample concordance lines above of the grammatical signal, however, and the lexical signal, pollution, thus exemplify the value of concordancing techniques to reveal common phraseologies, which may not be retrievable solely through intuitive means.
Conclusion This introductory chapter has laid out the theoretical groundwork for the means of identifying the Problem-Solution pattern in text through a clause relational approach to text analysis. It has also discussed the lexical and grammatical signals for identifying the Problem and Solution elements in various clause relations.
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Corpus-based Analyses of Problem-Solution Pattern
Most importantly, corpus-based techniques have been shown to be very useful for identifying the phraseologies of such signals for the Problem-Solution pattern. This approach therefore adopts a more discourse analytic perspective to corpus linguistics, an issue that is taken up in detail in the following chapter.
chapter 2
Issues in corpus linguistics and discourse studies
This chapter addresses several key issues in corpus linguistics and discourse analysis which are pertinent to the major themes of this book, namely, the methodologies employed, contextual features, and interpretation of data. Here I make the case that by taking a more discourse analytic approach to corpus-based investigations, some of these issues can, to a certain extent, be resolved. At the same time, corpus-based approaches also have advantages for discourse analysis (see Baker 2006: 10–17, for a succinct account of the advantages of the corpus-based approach to discourse analysis). McEnery et al. (2006) offer the following dichotomies of corpus linguistics vis-à-vis discourse analysis: …while DA emphasizes the integrity of text, corpus linguistics tends to use representative samples; while DA is primarily qualitative, corpus linguistics is essentially quantitative; while DA focuses on the contents expressed by language, corpus linguistics is interested in language per se; while the collector, transcriber and analyst are often the same person in DA, this is rarely the case in corpus lin(McEnery et al. 2006: 111) guistics…
In other words, the strengths of corpus linguistics tend to be the weaknesses of discourse analysis, and vice-versa. With reference to the quotation above ‘while DA focuses on the contents expressed by language, corpus linguistics is interested in language per se’, both approaches to text analysis could be considerably strengthened if, for example, the phraseologies uncovered through corpus linguistics techniques could fruitfully inform genre analysis, while genre analytic approaches could be applied to corpus-based analyses to shed light on the rhetorical aspects of text organization. This point is taken up in more detail in the following section on methodologies (see Biber et al. 2007 for studies which use corpus analysis to describe genre moves).
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Corpus-based Analyses of Problem-Solution Pattern
Methodologies Swales (2002, 2004) has commented on the methodologies that corpus linguistics employs (assuming that one accepts the basic premise that corpus linguistics is a methodology rather than a theory of language; see McEnery et al. 2006: 7–8 for a review of this argument). Swales argues that the software commonly used, namely concordancing packages for displaying the key-word-in-context, constrains analysis to a somewhat atomized, bottom-up type of investigation of the corpus data. This type of analysis is considered to be at odds with the more top-down kind of process-based analysis associated with the genre approach to text analysis, where the starting point is with the macrostructure of the text with a focus on larger units of text rather than sentence-level, lexico-grammatical patterning. Partington (1998) has called for a ‘symbiosis’ of these top-down and bottom-up strategies, which is evident in several recent corpus-based studies making use of the move structures of genre analysis (see Bhatia et al. 2004; Connor et al. 2002; Flowerdew 2008b). For example, Bhatia et al. (2004) examined some common verbs with their noun collocations in the four prototypical move structures in law cases (see Table 2-1). Bhatia et al. found that verbs had a clear preference for certain move structures, with submit in the move presenting argument often appearing in the patterning “counsel for the plaintiff/defendant submitted that…”, or “it was submitted that…”. This more quantitative approach of corpus linguistics can thus augment the more qualitative-based analyses of genre approaches. This more genre analytic approach to corpus analysis counteracts to some extent the following criticism made by Grabe and Kaplan (1996), who raised queries regarding corpus research on the grounds that the field lacks a theoretical foundation for the interpretation of data, thus implying that its methodological basis is somewhat open to question.
Table 2-1. Position of noun-verb collocations in law cases (Bhatia et al. 2004: 214) Genre Move
Frequency Reject
Submit
Dismiss
Grant
1 Facts / Stating history of the case 2 Presenting argument 3 Deriving ratio decidendi 4 Pronouncing judgment
75 263 5 3
47 6 16 42
12 9 44 9
82 51 80 16
Total
346
111
74
229
Chapter 2. Issues in corpus linguistics and discourse studies
The general dilemma facing most projects on corpus research is the lack of a theoretical foundation for the interpretation of the results prior to the analysis. Thus, most corpus research has been of a post-hoc nature, looking at the frequency counts and deciding what can be said about these results. (Grabe & Kaplan 1996: 46)
However, to date, this integration of corpus and genre approaches has been utilized only for those genres which exhibit a fairly formulaic, conventionalized rhetorical structure such as job application letters and law cases and for small corpora, as the data would have to be examined and coded manually for identification of move structures (Flowerdew 2005). Those texts comprising mixed genres or consisting of embedding of move structures would present a challenge for existing software, although software tools are becoming increasingly sophisticated and a tool such as WinMax has the flexibility to code embedded move structures.
Contextual features Another main argument that has been put forward against a corpus-based methodology for analysis of text is that it does not take account of the contextual features of text. As Widdowson (1998, 2002) points out, corpus data are but a sample of language, as opposed to an example of authentic language, because it is divorced from the communicative context in which it was created: ‘the text travels but the context does not travel with it’. In this respect, Tribble (2002) outlines an analytic framework for contextual analysis derived from a genre analytic perspective, which he views as crucial for informing corpus-based analyses. Tribble’s position, then, is to see the role of context as very much informing corpus-based analyses. Although the above features are really only a skeleton of the intricate, multidimensional contextual network expounded in recent genre studies (Bhatia 2004), even such rudimentary and essential contextual aspects are not usually taken into account in corpus investigations as the analyst does not have recourse to the communicative context in which the text was produced. However, more recently, spoken corpora such as the Michigan Corpus of Academic Spoken English (Simpson-Vlach & Leicher 2006) have been marked up for speech events and speaker attributes, thus allowing a more context-sensitive analysis of the data. It is this absence of context that poses one of the most serious drawbacks for the interpretation of concordance lines (Hunston 2002), another aspect of corpus linguistics that has been much debated in the literature.
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Corpus-based Analyses of Problem-Solution Pattern
Table 2-2. Analytic framework (Contextual) (Tribble 2002: 133) Contextual analysis 1. name 2. social context
3. communicative purpose 4. roles 5. cultural values 6. text context 7. formal text features
What is the name of the genre of which this text is an exemplar? In what social setting is this kind of text typically produced? What constraints and obligations does this setting impose on writers and readers? What is the communicative purpose of this text? What roles may be required of writers and readers in this genre? What shared cultural values may be required of writers and readers in this genre? What knowledge of other texts may be required of writers and readers in this genre? What shared knowledge of formal text features (conventions) is required to write effectively in this genre?
Interpretation of data This lack of knowledge of contextual features and social practices can be particularly problematic for the corpus analyst when dealing with pragmatic features of text, which may only be recoverable form the socio-cultural context or other features of the text, as noted by Widdowson: …on the evidence of their customary collocates, particular words can be shown to have a typical positive or negative semantic prosody, and it can be plausibly suggested that facts of co-textual co-occurrence should be recognized as part of the semantic signification of such words. But this, of course, does not tell us about what pragmatic significance [my italics] might be assigned to such a cooccurrence in a particular text. The point about these co-textual findings is that they are a function of analysis, with texts necessarily reduced to concordance lines. One might trace a particular line back to its text of origin, but then if it is to be interpreted, it has to be related to other features of the original text. (Widdowson 2004: 60)
However, in a paper on corpus semantics Stubbs (2001a) argues that the conventionalized view that pragmatic meanings are usually inferred by the reader/listener, making them highly context-dependent, may be overstated and that largescale corpus studies can provide evidence to show that pragmatic meanings, like semantic prosodies, can also be conventionally encoded in linguistic form. This, though, may depend on the type of corpus under investigation and whether one has knowledge of the discursive conventions of the genre. In this respect, Bhatia et al. (2004) point out that the two verbs dismiss and reject used in law cases (see
Chapter 2. Issues in corpus linguistics and discourse studies
Table 2-3. Concordance lines for ‘United States of Europe’ (adapted from O’Halloran & Coffin 2004: 288) leader’s bleak plan for a the road towards a Federal forming into a giant The empire builders want a thirds say there will be a for a hopeless dream of a was the first step to a or a state in a newly-formed Just as many are against
United States of Europe United States of Europe. United States of Europe United States of Europe. United States of Europe United States of Europe. United States of Europe United States of Europe? United States of Europe
came as a hammer blow to Hague has never tried to – with the same tax and Thank goodness you have within the next 20 years. He is certain to pay the – which would cost These are the central under a federal
Table 2-1) appear to be almost synonymous semantically, but that if one wanted to make a pragmatic distinction between them, it would be necessary to look at the institutional and discursive practices of this genre. Evidence in support of Stubbs’ view is provided by O’Halloran and Coffin’s (2004) research motivated by critical discourse analysis (CDA) approaches. Based on a 45-million-word sub-corpus of the Sun newspaper drawn from the 450-million-word Bank of English, O’Halloran and Coffin show how negative attitudinal meaning can be gleaned from multiple concordance lines; an accumulation of negative co-texts for United States of Europe displays a regular negative attitude for ‘United States of Europe’, thus reflecting the anti-Europe stance of the Sun newspaper. Such an ideological stance may not be immediately obvious when encountered as a single instance, but can be retrieved from examining the co-textual environment of repeated occurrences of the search word in a large corpus. CDA approaches to text analysis often employ various categories from Halliday’s systemic functional grammar (1994), most notably the aspects of transitivity and nominalization. In their research, O’Halloran and Coffin made use of experiential meanings to uncover negative stance. Using concordancing techniques they uncover a pattern where Brussels or the EU are primary ‘doers’, and when the EU is the implicit Initiator, Britain is an Actor carrying out an activity initiated by the EU: ‘The continual reinforcement of this pattern helps to establish the experiential meaning in the text of Britain as powerless in the face of the EU’ (p. 283). The following examples illustrate this stance. …Brussels aimed to snatch power over UK employment, foreign affairs… Actor [ Goal ] …Britain would be forced to surrender control of its economy to Brussels [by the EU] Actor [ Goal ] implicit initiator ]
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Corpus-based Analyses of Problem-Solution Pattern
Here, in contrast to Tribble’s viewpoint that contextual features of a genre analytic approach inform the corpus analysis, we have another perspective from critical discourse analysis (CDA) whereby corpus data is viewed as shedding light on the social and cultural context from which the corpus is extracted, as exemplified by Table 2-3 and the examples above. This is in line with Halliday’s system of language as a social semiotic, which CDA leans heavily on. The repetitions of linguistic patterns in the co-text, revealed by co-selection of items on the vertical axis of the concordance lines, reflect the context, i.e. the situational and cultural parameters involved in the creation of meaning (Tognini-Bonelli 2001, 2004). As Blommaert (2005: 66) notes: ‘We should be looking at how the linguistic generates the economic, social, political, as well as how the economic, social, and political generate the linguistic’. One could say that, generally speaking, a CDA approach to corpus analysis achieves the first goal, while a genre-analytic approach meets the second of Blommaert’s aims. But even the traditional distinctions between CDA and genre (in the Swalesian sense) are becoming blurred with the recent work of Bhatia proposing the need for a critical genre analytic approach to the understanding of discursive practices, which rely on the bending of generic norms to present a certain ideological positioning (see Bhatia 2008 for a critical analysis of a corpus of corporate letters to shareholders). It should be noted that the field of CDA, which in general does not make use of corpus linguistic techniques, has been singled out for its cognitive biases, i.e. ‘reading too much’ into individual texts and assigning ideological significance to co-textual relations on very scant evidence and pure conjecturing (cf. Blommaert 2005; Widdowson 2004). Widdowson takes Fairclough (1995) to task for assigning the co-occurrence of ‘flock’ and ‘people’ a passive signification only through an intuitive inclination for linking ‘flock’ with ‘sheep’. Widdowson (2004: 110) concludes that: ‘CDA might more profitably draw on an approach to linguistic description that deals with texts in their entirety and takes explicit account of cotextual relations. Corpus analysis is just such an approach…’; the corpus-based CDA research of O’Halloran and Coffin bears out this statement and points the way for future interdisciplinary research. It is interesting to note that in a major textbook on the methods of CDA (Titscher et al. 2000), there are scant references to ‘corpus’, which does not even appear in the index. However, co-occurrence of items in recurring concordance lines still has to be interpreted and, as Baker (2006) points out, a potential problem lies in the interpretation being open to contestation. By way of example, Baker (2006: 18) cites a study by Rayson et al. (1997) which found that people from socially disadvantaged groups used more non-standard language (e.g. ain’t) and taboo terms (e.g. bloody) than people from more advantaged groups. Baker notes that: ‘while the results aren’t open to negotiation, the reasons behind them are’, commenting
Chapter 2. Issues in corpus linguistics and discourse studies
that the analyst could arrive at a number of conclusions to explain the data (e.g. upbringing, using the terms to show group identity and solidarity), based on their own biases and identities. A check against potential misinterpretation would be to validate one’s interpretation with ‘specialist informants’ who are members of a particular discourse community familiar with its discursive practices. Hyland (1998), for example, consulted four native-speaker biologists on the use of hedging devices in a corpus of 80 research articles in cell and molecular biology. He asked them to voice their reactions to underlined features in the text and had them participate in small focus group discussions to elucidate why the ‘expert’ writing under investigation was appropriately phrased for readers. This more ethnographic dimension to genre, involving data-gathering procedures such as participant observation and input from subject specialists, is usually associated with the New Rhetoric approach to genre studies, where the focus is very much on looking at how various aspects of the socio-cultural dimension shape the genre.
Corpus linguistics: Towards a multi-faceted approach Chapter 1 laid out the theoretical underpinning of the Problem-Solution pattern and illustrated via selected concordance lines for the items however and pollution how a more discourse-analytic perspective, drawing on aspects of the ProblemSolution pattern, could inform the field of corpus linguistics. This chapter has illustrated how other areas of discourse studies, namely genre and CDA, which view text as socially-situated, can enhance the field of corpus linguistics, especially with regard to contextual issues and interpretation of the data. At the same time, these three approaches to text analysis can profit from corpus methodologies which provide quantitative data in the form of recurring phraseologies as evidence for different elements of the Problem-Solution pattern, certain ideological stances in CDA or prototypical move structures in genre studies. Based on the lacunae identified between corpus linguistics and various subfields of discourse analysis (cf. Flowerdew 1998a) this book aims to suggest how more of a symbiosis between these interdisciplinary fields can be achieved. Specifically, this book will deal in detail with the following question, which is one of the main foci of the book: – How can elements of the Problem-Solution pattern be identified through corpus linguistic methodologies?
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20 Corpus-based Analyses of Problem-Solution Pattern
In more general terms, the book will also consider the following aspects in the phraseological analysis of elements for the Problem-Solution pattern, briefly overviewed in Chapter 1. – What aspects of Halliday’s systemic-functional grammar may be useful for analysis of the corpus data? – How can insights from genre analysis aid interpretation of the data? – What can recurring patterns in the data tell us about the discursive practices of the genre? This book presents a small-scale research study which seeks to apply insights from discourse studies and corpus linguistics with a view to moving towards a more multi-faceted analysis of corpus data. As a result, the dichotomies between the two fields, as highlighted in the quotation from McEnery et al. (2006) at the beginning of this chapter, will not be so pronounced and, by extension, the issues raised regarding their respective weaknesses also less conspicuous.
chapter 3
The two corpora Context and compilation
This chapter first describes the two corpora on which the research is based, with particular reference to their contextual features (e.g. audience, communicative purpose), as these are significant factors in shaping the discourse. Various aspects of the compilation and preparation of the corpora for subsequent analysis are then described. In this regard, of particular importance are the issues of corpus size and representativeness, identification of types and lemmatization.
Contextual background of the Professional and Student corpus Professional Corpus (PROFCORP) The professional corpus (PROFCORP) comprises 60 professional reports on environmental issues. The majority of these reports are the executive summaries of Environmental Impact Assessment (EIA) reports commissioned from the early to mid 1990’s by the Hong Kong Environmental Protection Department (EPD) or Civil Engineering Department from various environmental consultancy companies in Hong Kong. These are solicited reports, written in response to a ‘Request for Proposal’, which document the potential environmental impacts that could arise from the construction and operation of proposed buildings/facilities. The aim of the reports is to suggest possible mitigation measures which could be implemented to alleviate any possible adverse environmental effects. It is to be noted that most of the companies specify a template for structuring the reports, so it is not uncommon to find variations of the main headings ‘Environmental Impacts’ and ‘Mitigation Measures’ across many of the reports. In some cases, the reports are co-authored, but they are always checked over and endorsed by a senior engineer before being submitted to the EPD. They are written by both native speakers and non-native speakers of English, although in the Hong Kong context care is needed in defining the concept of non-native speaker. Some of the engineers working in these companies are referred to as ABC (American-Born Chinese), while others have undertaken their tertiary edu-
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Corpus-based Analyses of Problem-Solution Pattern
cation, and possibly their secondary schooling in the States, UK or Canada. As a result, they have English which is almost indistinguishable from that of educated speakers of those afore-mentioned countries. However, what is at issue here is not so much whether the writers are native or non-native speakers, but whether they are competent writers of the type of written professional documentation under investigation. It should be noted that this data collection of the EIA reports took place before the 1997 handover when Hong Kong was a British territory and UK-based consultancy companies dominated the bidding for government contracts. The senior engineer-in-charge who vetted the final version of the reports would have been British and therefore these reports can be considered as written in British English. This background information has important implications for the choice of a contrastive reference corpus, which is discussed in Chapter 5. The titles of these reports together with a breakdown of the number of words in each and the consultancy firms who produced them are given in Appendix 3-1. Each report was given a filename, e.g. 1_ERM, which could be used to identify the consulting company who produced the report and to differentiate one report from another written by the same company.
Student Corpus (STUCORP) The student corpus (STUCORP) comprises 80 group project reports written by 2nd and 3rd year undergraduate Science and Engineering students on a Technical Communication Skills course in the Language Centre at the Hong Kong University of Science and Technology (HKUST). For this group project the assignment guidelines stipulate that students are expected to choose an area for investigation where a problem or need can be identified on the basis of evidence from secondary and primary source data (survey questionnaire, interview, observation), and propose a set of recommendations on the basis of the identified problem or need. No templates are provided in order to discourage students from over-relying on ‘model examples’, although the in-house produced student textbook does give several examples of reports, which the students can draw on for their own project reports. Instead of providing a template, the student textbook reviews different organisational structures (i.e. part-by-part, or whole-by-whole) and types of subheadings (i.e. structural, topical) with the aim of encouraging students to choose the most appropriate one for their report. All the topics of the student reports in STUCORP differ from those of the professional reports in PROFCORP, as they relate to the university and mostly concern departmental or service unit issues which are of importance to the students in some way. The titles of the student reports together with a breakdown
Chapter 3. The two corpora
of the number of words in each and the general topic areas which they cover are given in Appendix 3-2. As with all the reports in PROFCORP, each report was assigned a filename, e.g. 1_CS, which could identify the topic of the report, with the first digit in the filename used to distinguish one report from another on the same topic. In one sense, the student reports can be considered as solicited as they are an assessed assignment as part of an academic requirement. In another sense, though, unlike the EIA reports in PROFCORP, these reports are unsolicited in that the students write the report on the basis of a problem perceived by them rather than in response to a request by a department to investigate an issue. Because the reports are unsolicited, the students have to make a strong case for the existence of a problem, as the departments concerned either might not be aware that a problem exists, not realise its import, or may not agree that there is a problem. This is why most of the material in the student textbook is devoted to the aspect of providing evidence for a problem through gathering data from primary and secondary sources. Moreover, although these reports are of a technical nature, the guidelines specify that the report must be written for management, i.e. a non-specialist audience. Appendix 3-3 presents the rubrics for the assignments and some extracts from the textbook designed to sensitise students to key aspects of the project reports. Although PROFCORP and STUCORP are the product of two different discourse communities, the two corpora are, in fact, similar in two main respects: length and text type. First of all, the two corpora are of comparable size – each contains approximately 225,000 running words (see Appendices 3-1 and 3-2). Secondly, the fact that the reports in PROFCORP fall under the category of Environmental Impact Assessment reports implies that they are recommendation-based by virtue of their text type as an environmental problem is identified for future mitigation. Likewise, the reports in STUCORP can also be regarded as belonging to the Problem-Solution text type because, as mentioned previously, the assignment guidelines specify this discourse structure for the reports, which is also in evidence in some of the titles, sometimes with the focus on the problem aspect (cf. report no. 23 in Appendix 3-2 entitled Investigation of sports facilities) or with the focus on the solution aspect (cf. report no. 6 entitled Installing computer terminals in UST campus). Although some of the titles may only reflect the solution aspect, the body of the reports should provide evidence for an existing problem as this is stipulated in the assignment guidelines. However, at this stage, we cannot state categorically that these reports are Problem-Solution based as we only have external evidence for this, i.e. in the form of the report guidelines, stipulating the audience and purpose. As Lee (2001) notes, text categorizations are generally based on ‘external’ criteria, i.e. where and when the text was produced, by and for
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Corpus-based Analyses of Problem-Solution Pattern
whom and its communicative purpose, rather than ‘internal’ criteria based on its linguistic characteristics. The purpose of the analysis in Chapter 4 is to provide linguistic evidence for classifying the reports in PROFCORP and STUCORP as belonging to the Problem-Solution pattern. In sum, PROFCORP and STUCORP can therefore both be regarded as ‘specialised’ on the grounds that they are delimited by a specific text type, discourse domain and have been compiled with an a priori purpose in mind. (see Flowerdew 2004a for a detailed discussion on the notion of ‘specialised’). This is an example of what Sinclair (2001: xi) refers to as the early human intervention (EHI) method – as opposed to the late or delayed human intervention (DHI) associated with large-scale corpus analysis – where the analysts have a clear goal at the outset and thus construct a corpus and decide on the methodology with a specific purpose in mind. In the following sections various aspects concerning compilation of the two corpora are discussed with reference to issues raised in the literature. Methodological issues regarding identification of types and lemmatization, which are related not only to the type of corpus under investigation, but also to the line of linguistic enquiry, are also addressed.
Issues in corpus compilation Size and representativeness Several corpus linguists have raised issues concerning the size and representativeness of specialized corpora. In fact, these are thorny issues, which have also been widely debated in the literature on corpus studies in general, and to which there seem to be no easy answers. A commonly held view is that the larger the corpus, the better it is for extracting linguistic information: ‘Regarding the question of corpus size, writers are unanimous in arguing that in principle bigger is better (Sinclair 1991). The more text there is in a corpus, the more likely it is to give an accurate representation of the language and an adequate number of examples of a given key word’ (Flowerdew 1996: 100). While this is true in general terms, this whole question of what is considered to be an appropriate size for a corpus is highly dependent on the phenomenon one is investigating. As other researchers have pointed out (de Haan 1992), there is no ideal size for a corpus and the suitability of the sample depends on the specific study that is undertaken and the needs and purposes of the investigation.
Chapter 3. The two corpora
Table 3-1. Comparison of frequencies in a general and a specialised corpus (Sinclair 2005: 15) Number of different word-forms (types) Number that occur once only Number that occur twice only Twenty times or more 200 times or more
LOB
HK
%
69990 36796 9890 4750 471
27210 11430 3837 3811 687
39% 31% 39% *0% (69%)
With regard to the investigation of specific items, McEnery and Wilson (2001: 154) point out that the lower the frequency of the feature one wishes to investigate, the larger the corpus should be. This would apply to nouns, adjectives, adverbs etc, (i.e. content words) which tend to have a much lower frequency than grammatical words in any given corpus. Conversely, one can argue that smaller corpora can be used for investigating the more common features of language such as grammatical items, and indeed, Biber (1990) has pointed out that smaller corpora are perfectly adequate for purposes such as these. The size of the corpus is therefore of paramount importance and must be closely matched with the features under investigation. However, here again, size has to be balanced against the level of delicacy of the investigation, an issue touched upon in Kennedy (1998), who remarks on the danger of having too much output such that the data are unwieldy to work with. Sinclair (2005) makes a very strong case for size not being such an issue as far as small, specialized corpora are concerned. Evidence for this point is based on a comparison of frequencies across two one-million-word corpora: LOB, a general corpus, and the Hong Kong corpus of the English of Computing Science, as shown in Table 3-1. Sinclair comments thus: This is only one example, but it is good news for builders of specialised corpora, in that not only are they likely to contain fewer words in all, but it seems as if the characteristic vocabulary of the special area is prominently featured in the frequency lists, and therefore that a much smaller corpus will be needed for typical studies than is needed for a general view of the language. (Sinclair 2005: 15)
Sinclair’s frequency-based evidence that specialised corpora, by their very nature, do not exhibit as much internal variation as general corpora, is a factor that has implications for not only the size of the corpus but also its representativeness. The greater the variation in the corpus text under study, the more samples and a larger corpus are required to ensure representativeness and thus validity of the data (Meyer 2002). See also McEnery and Wilson (2001: 63–66), for a detailed
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26 Corpus-based Analyses of Problem-Solution Pattern
discussion on corpus representativeness. In respect of this issue, it is well to heed the words of Tognini-Bonelli (2001: 57): ‘We should always bear in mind that the assumption of representativeness “must be regarded largely as an act of faith” (Leech 1991: 27), as at present we have no means of ensuring it, or even evaluating it objectively (see also Sinclair 1991: 9). This vexing issue of corpus representativeness could be regarded as more crucial as far as specialized corpora are concerned on account of the fact that the representativeness of specialized corpora is usually measured by reference to external selection criteria (i.e. by/for whom the text is produced, what is its subject matter), which could be regarded as somewhat subjective. On the other hand, Williams (2002) sees one way round this dilemma by making a case for using internal selection criteria based on lexical items, which he argues is a more objective means of ensuring the representativity of specialized corpora. A complicating factor is that often pragmatic factors, such as how easily the data can be obtained come into play, i.e. the compiler has to fall back on non-probability sampling techniques involving “judgement” and “convenience” (Meyer 2002: 44). That being said, it is a sine qua non that a specialized corpus should be of adequate size such that there is a sufficient number of occurrences of a linguistic structure or pattern to ensure representativeness for validating a hypothesis. Insofar as the compilation of PROFCORP and STUCORP are concerned, an effort has been made to ensure that the corpora are as representative as possible for the type of writing under investigation. For example, the 60 reports in PROFCORP were partly selected on the basis that they represent 23 different consulting companies, thus ensuring no one company style would dominate. However, it was impossible to select an equal number of reports from each of the companies as access to these was dependent on which ones were available in the public libraries. As a general rule, though, the larger the company, the more reports were catalogued in the libraries, so the distribution of reports in PROFCORP can be seen as reflecting the size of the company, which could also be regarded as another aspect of representativeness. The data collection therefore relies on a combination of “judgement” and “convenience” sampling; every effort was made to collect reports from as wide a range of companies as possible, but ultimately this depended on what was available in the public libraries in Hong Kong (see Meyer 2002: 42–43, who discusses different types of sampling frames). As for the 80 reports in STUCORP, 25 different main topic areas are represented. Some of the topic choices are more popular with students than others, but again this is represented by their distribution in the corpus. As regards the argument of using internal selection criteria as a more objective means of ensuring the representativity of specialised corpora, in Chapter 4, I will demonstrate by internal criteria that both corpora are Problem-Solution ori-
Chapter 3. The two corpora
ented and therefore contain sufficient examples for investigation of the linguistic structures realising this particular text type. Moreover, as each corpus comprises approximately 225,000 words, they do not yield a quantity of output that is overwhelming to work with, an important point noted by Kennedy earlier.
Identification of types A type is defined as each different word form whereas a token is an individual occurrence of any word form, i.e. type (Barnbrook 1996: 53). One noticeable difference between these reports is that the PROFCORP consists of 8,724 different types whereas the STUCORP has 7,268 different types. This result runs counter to what I expected as given the diverse topics of the student reports compared with the focus on a particular topic (environmental assessment) of the professional reports, I would have predicted the student reports to contain more different types. The linguistic analysis of key words in Chapter 4 sheds light on this phenomenon. First of all, though, it is of crucial importance to consider what constitutes a ‘type’ at the outset as this will have a bearing on the subsequent analysis of the corpus. In the following sub-sections, I discuss decisions made regarding specifications of types, or word boundaries. As the reports in both PROFCORP and STUCORP contain multi-word nouns, Latin abbreviations and cases where sometimes an abbreviated form of a word is used but at other times the full form, it is necessary to have a consistent policy on how to handle these.
Multi-word proper nouns Multi-word proper nouns, denoting names of countries, islands, towns, roads, buildings, airports and names of people, were treated as one semantic unit, i.e. one type. In order for the software to count these nouns as one type, the symbol 0 was used in place of spaces. So, for example, Hong Kong would be shown as Hong0Kong. Not surprisingly, PROFCORP was found to contain a wider range of examples of such nouns than STUCORP as the professional reports refer to entities within the whole of Hong Kong, whereas the student reports are mostly related to university departments and service units. All the proper nouns were identified by three means. First, they were identified by manually skimming through the printed output of the corpora. Changes were then made in the computerised version of the corpora, with the assistance of the search command. Finally, an alphabetical wordlist was manually checked to verify that all instances of proper nouns had been collected.
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Latin abbreviations In a draft wordlist, it was noted that there were occurrences of single letters, which required further investigation. It was found that these isolated cases of single letters were Latin abbreviations in either small or upper case (N.B.; I.E.; e.g.; p.m.). As there was a full stop separating the two parts, the program was obviously reading some of the Latin abbreviations as two separate words. The full stop was removed so all Latin abbreviations would be read as one type. Abbreviations vs. full forms This category concerns those items where the full form is used in some parts of the text (e.g. Environmental Protection Department) but the abbreviated form in other parts (e.g. EPD) for synonymous pairs of semantically-related items. Three options are possible for dealing with this situation: (i) treat EPD as one item and Environmental Protection Department as three (ii) treat EPD as three items and Environmental Protection Department also as three (iii) treat EPD as one item and Environmental Protection Department also as one. The simplest solution would be to opt for (i) if EPD is seen as functioning as an anaphoric/exophoric element, in fact, similar to a pronoun which also either refers the reader back to a multi-word phrase or to an entitiy identifiable on the basis of real world knowledge. This would be feasible with the PROFCORP reports where the abbreviations used, such as EPD, conform to the convention of using the full form for the first mention, with the abbreviation noted in parentheses and using the abbreviated form hereafter in the text. However, this standard usage for full forms and abbreviations was not observed in the STUCORP reports. Students mixed full forms and abbreviated forms quite indiscriminately in their reports with the result that the function of an abbreviated form having an anaphoric function was distinctly blurred, as this was often taken up by the full form. As it is imperative to apply the same criteria to both corpora for identifying word types, option (i) is therefore rejected on account of the arbitrary use of abbreviations in the STUCORP. Another possibility was option (ii), where EPD is expanded into three items. This option was also rejected as although semantically it would be a legitimate strategy, it contravenes one’s sense that EPD functions as one entity. I therefore decided to adopt option (iii) which does allow EPD to be considered as one entity. Another reason for choosing option (iii) is that it can also accommodate the anomalous use of the full forms in the STUCORP reports (which very often have an anaphoric function usually taken up by the abbreviated form) by treating them
Chapter 3. The two corpora
as one item. Sometimes, it is not clear cut as to how many items the full form consists of (cf. Electronic Notice Board and Electronic Noticeboard), but this is not an issue if option (iii) is chosen. Apart from EPD used for Environmental Protection Department and EIA for Environmental Impact Assessment reports, very few other abbreviations were found in the PROFCORP. However, in the STUCORP, four main categories of usage were identified, which, as can be seen from the examples below, mainly refer to HKUST entities. As with the proper nouns, the separate words in the full forms were joined by using the symbol 0.
Departments Centre of Computing Services and Telecommunications (CCST) Safety and Environmental Protection Office (SEPO) Student Affairs Office (SAO)
Type of student according to discipline Computer Science (CS) Computer Engineering (CPEG) Electrical and Electronic Engineering (EEE)
Universities Hong Kong University of Science and Technology (HKUST) Chinese University of Hong Kong (CUHK)
Internet features World Wide Web (WWW) Internet Service Providers (ISP) Electronic Noticeboard (ENB)
Obviously, the above is quite a time-consuming process as all these adjustments are made manually. In this respect, the reader is referred to work being carried out at the University of Liverpool (cf. Renouf 1996 for details of the ACRONYM project), where corpus tools are being developed for the automatic identification of thesaurally-equivalent terms such as those listed above. This project is referred to in Chapter 5 with respect to synonymous and hyponymous relations existing between keyword lexis for the Problem-Solution pattern. In addition to the decisions made above to count multi-word proper nouns, Latin abbreviations and the full forms of their corresponding abbreviations as one type, another issue which arose was how to deal with the inconsistencies in the handling of characters found within words. Such characters refer to hyphenated words and apostrophes used in short forms of verbs, auxiliaries, and negatives, which are discussed in detail below.
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Characters found within words Many inconsistencies in hyphenation occur across the texts and sometimes three different variations of the same item were found, e.g. back-up, back up and also backup. As the hyphenated words outnumber their two-word counterparts, these were therefore considered as the core, or unmarked, items, so to speak. For this reason, all words which are hyphenated in the original text are treated as having the default value of a single word. So, for example, both back-up and backup are treated as the same type. However, the Wordlist tool identifies back up as two separate types. These typographical variations therefore result in some minor anomalies, but it was felt that any attempt to categorise items with variant spelling such as back-up, back up and backup as a single type would be a time-consuming process which would not result in much gain. Like the hyphen, the apostrophe is another character that can occur within a word in short forms of verbs, auxiliaries and negatives such as It’s, you’re, can’t and don’t. All contractions were left as they were rather than being restored to their full form for two reasons. First, it is useful to retain the contractions in the original as they reflect stylistic choices of the author(s). For example, there were no instances of the short form don’t in PROFCORP but 25 occurred in STUCORP which is an indication of the different levels of formality in the reports. Secondly, it would be very time-consuming to make adjustments manually in the corpora. Bruthiaux (1996), who discusses the issue of hyphenation and apostrophes (pp. 33–34), trawled through his corpus to manually separate all contractions marked by an apostrophe (I’m becoming I’ m, for example) so that such contractions were treated as two words. This is possible with his relatively small corpus of advertisements (16,075 words) but would be very laborious in larger corpora. Thus, the size of a corpus is another vital consideration as to whether certain text adjustments are expedient or not. It was also noted that there were five misuses of the apostrophe s in STUCORP, with it’s substituted for possessive its, but these misspellings were also retained in accordance with my desire to tamper with the text as little as possible. I therefore did not change any of the output produced by Wordsmith’s Wordlist relating to characters, i.e. hyphens and apostrophes within words, for ease of expediency. Neither did I attempt to standardise words which are identical phonologically and lexically, but not orthographically (i.e. those words which can take either American or British spelling, e.g. organize vs. organise) as this would have meant making adjustments in the text, rather than relying on the Wordlist output, and in any case such adjustments would have made very little difference in the subsequent analysis. One area, though, where identification of types is of utmost importance in the type of lexico-grammatical analysis undertaken in this corpus analysis is that
Chapter 3. The two corpora
of lemmatization. In the following section I first define lemmatization, and then explain my rationale for not lemmatizing the corpus.
Lemmatization A lemma is usually considered as the base or uninflected form of a word (Biber et al. 1998: 29). As Sinclair (1991: 42) points out ‘Traditionally, the ‘base’, or uninflected form is used even when that form is hardly ever found on its own, or hardly ever found at all’. Here the word ‘Traditionally’ seems to be setting up an objection to this definition, which Sinclair provides later in the text when he puts forward the suggestion that the most frequently-encountered form could equally well be regarded as the lemma. Lemmatization is defined as follows in Sinclair (1991): Lemmatization is the process of gathering word-forms and arranging them into lemmas or lemmata. So the word-forms give, gives, gave, given, giving, and probably to give, will conventionally be lemmatized into the lemma give. Any occurrence of any of the six forms will be regarded as an occurrence of the lemma. (Sinclair 1991: 173)
However, just as the word ‘traditionally’ implies that defining a lemma is not a straightforward procedure, the word ‘conventionally’ also hints that lemmatization may not be as simple and obvious a process as it at first appears either. This reservation towards lemmatization is echoed in Sinclair (1992: 390–391) ‘… it is conventional to think of meaning as constant across different inflected forms of a word; in such cases the inflections could be conflated together into lemmas and a lemmatiser used to do the job’. Counter-examples to this assumption are given in Sinclair and Renouf (1988) who provide corpus evidence to demonstrate that the morphological pair certain and certainly behave quite independently of each other in terms of meaning and usage patterns. (In fact, the examples of certain and certainly would not be considered by some as even belonging to the same lemma as they are not of the same word class as one is adjectival and the other adverbial). At a greater degree of specificity regarding meaning, Tognini-Bonelli (2001: 92–98, cited in Knowles & Don 2005) questions whether facing and faced should be assigned to the lemma ‘face’ as the former has a concrete meaning (e.g. facing forwards) in addition to its metaphorical meaning (e.g. facing a dilemma), whereas the latter retains only the metaphorical meaning (e.g. faced with a dilemma). Therefore, even the notion of what constitutes a lemma is debatable from a meaning potential point of view.
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Another problem associated with lemmatization is that different forms of a lemma pattern differently, in terms of collocation and colligation (Barnbrook 1996: 50; Stubbs 1996: 37). Stubbs (1996: 38) cites Sinclair’s (1991) example of the lemma SET, of which the verbal form set was found to be much commoner than the other forms and commonest in past tense use, with SET IN tending to occur in end of sentence position. These are colligational preferences. Stubbs (1996) has also shown how different lemmas can also have different collocates. For example, of the lemma EDUCATE the form education mainly collocates with terms denoting institutions (e.g. further, higher, university) whereas the form educate collocates with the near synonyms enlighten, help, inform. Sinclair (1992) proposes that examining the collocational patterns of different word forms can be used as a basis for deciding whether they belong to the same lemma or not. Another consideration is the fact that different forms of a lemma may possibly have different semantic prosodies (positive or negative connotations associated with a word, Louw 1993). It has been pointed out above that different forms of a lemma have different frequencies in a corpus, may have different meanings, different colligational and collocational patterning, may occupy different positions in the sentence, and may possibly have different semantic prosodies (see Hoey 1997 for an elaboration of these aspects). As one of the main aims of this research is to examine the Problem-Solution pattern from a phraseological perspective, it would therefore be counterproductive to lemmatize the corpus automatically.
Conclusion This chapter has described the background and contextual features of the two corpora, including a brief overview of the literacy practices and processes (Barton 2000) in which the writing was constructed. It has also addressed the issues of corpus size and representativeness, with a justification of the adequacy of the two corpora in this respect. The chapter has also detailed the methodological procedures undertaken in compiling the corpora for analysis. From the above discussion on identification of types and lemmatization it can be seen that I have largely adhered to Sinclair’s (1991: 21) clean-text policy: ‘The safest policy is to keep the text as it is, unprocessed and clean of any other codes. These can be added for investigation’. I have made the minimal amount of text adjustments in accordance with Sinclair’s clean-text policy as my aim is not to obtain statistical information from large amounts of text, but rather to use statistical tabulation of the corpus evidence as a starting point for qualitative analysis of specific features in two specialised corpora, using the Concord and KeyWords tools.
chapter 4
Frequency, key word and key-key word analysis of signals for the Problem-Solution pattern
The first section of this chapter describes the Appraisal framework of Inscribed and Evoking categories, borrowing from systemic-functional linguistics, for classifying the signals of the Problem Solution pattern. The second part presents a frequency, keyword and key-key word analysis of the signals, classified according to Inscribed and Evoking items, with particular attention paid to their interface with another categorisation of lexis, technical and sub-technical vocabulary. As was noted in the previous chapter, although all the texts in PROFCORP are labelled as ‘environmental audit’, which by its very definition implies a type of recommendation report, and this is evidenced by many of the headings such as ‘Recommendations’, we still need concrete evidence as proof for this. The same also applies to the reports in STUCORP, many of which are also explicitly labelled as recommendation-based by virtue of their titles and by the guidelines given to students for this writing task. Identification of the signals for the Problem-Solution pattern through computational techniques would provide internal linguistic evidence for classifying both the PROFCORP and STUCORP as Problem-Solution based, which as noted in the previous chapter, is a more reliable indicator of representativeness than relying solely on external criteria.
Classificatory framework for signals: Appraisal system Signals for the Problem-Solution pattern, by their very nature, are evaluative and thus there needs to be a categorization which accommodates this inherent quality. It was therefore felt that utilising the Appraisal system for encoding attitude from the systemic-functional tradition (Martin 2000, 2003) would provide an ideal framework.
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Inscribed vs. Evoking items According to the Appraisal system, items are classified as Inscribed or Evoking. The Inscribed option realises lexis which is explicitly evaluative. This would correspond to the items in Proctor’s list referred to in Chapter 1, such as problem, fault, drawback, where the evaluation is built into the word, as it were. Carter (1992) also recognises this evaluative quality inherent in some of these Inscribed Vocabulary 3 items which serve the same function as Francis’ A-nouns mentioned in Chapter 1: ‘An interesting category of A-nouns are those which generally signal attitudes. Such items do more than merely label the preceding discourse. They mark it in an interpersonally sensitive way revealing the writer’s positive or negative evaluation of the antecedent proposition’ (p. 80). Here, Carter touches upon an important distinction between Inscribed and Evoking lexis regarding the reader/writer orientation towards the text, as also pointed out in Hoey (2001): ‘The writer inscribes the evaluation; on the other hand, it is the word that evokes (or provokes) an evaluation in the reader’ (p. 126). The Evoking option ‘draws on ideational meaning to ‘connote’ evaluation, either by selecting meanings which invite a reaction or deploying imagery to provoke a stance’ (Martin 2003: 18). In this model, it is the ‘invite’ option of the Evoking category I am interested in, where the item, taken out of context, would evoke an evaluative response in the reader. For example, items such as cancer and dust in Jordan’s list, which as Proctor argues are lexical realisations of the P. [Problem] signal rather than signals by and of themselves, would belong to this category. Yet another option would seem to exist for the Evoking category where it would only be possible to tell from the context whether an item such as landfill evokes a positive or negative semantic prosody (see Thompson & Hunston 2000 for a discussion of the role of context in bringing out this element of evaluation). Although Partington (2001) does not use the terms Inscribed and Evoking in his discussion on investigating connotation, Inscribed lexis seems to be what he is referring to when he mentions that where connotation is so intrinsic to a word it is taken for granted, i.e. writer-initiated, and Evoking lexis when he discusses examples where the connotation seems less intrinsic, which can be based on situational or cultural factors. However, the distinctions between Inscribed and Evoking evaluative lexis are by no means as clear-cut as the definitions above seem to suggest. Most of the items considered as Inscribed lexis, where the word is intrinsically evaluative, would be superordinate categories such as problem, solution, but this category could also encompass more specific terms such as inefficient, unsatisfied, where the evaluation is explicitly signaled by adjectival prefixes such as in-, un- or non-. The Evoking category, which covers items where the evaluation is less intrinsic,
Chapter 4. Frequency, key word and key-key word analysis
also raises queries for classification of evaluative lexis. In this category, I would also include items such as pollution for the reason that, although such lexis does have an intrinsic negative connotation, which would seem to qualify it for membership of the Inscribed class, it is not a superordinate term, but rather acting as a hyponym of problem. I would therefore like to argue that as lexis such as problem and pollution have different hyponymic status, they should be accorded a different classification. Other terms, such as noise clearly fit into the Evoking category as their negative connotation is, to a large extent, induced by the reader’s interpretation of it. It would seem that the difficulties with Martin’s classification mainly arise from the fact that this kind of evaluative lexis occurs more along a cline and does not easily lend itself to being shoehorned into two discrete categories, a point also discussed in Flowerdew (2003, 2004b). Nevertheless, in spite of these classification difficulties, it has much to recommend it in highlighting the overall patterning of different kinds of evaluative lexis in the two corpora (see Hunston 1993, 1994 for detailed descriptions of evaluation in scientific text and Camiciotti & Tognini-Bonelli (eds.) 2004 for discussions on conceptualisation and recognition of evaluation).
Frequency analysis of signals Starting at a very general level, it would be useful to compare the 100 most frequent words in STUCORP and PROFCORP with the 100 most frequent words in two general large-scale corpora covering a wide variety of genres. The general corpora chosen were COBUILD listed in Sinclair (1986: 192) and the core written component of the BNC (http://info.ox.ac.uk/bnc) as these two corpora contain a wide variety of genres and are the most recent large-scale corpora available, although compiled in the 1990s. The aim of using this approach is to get a very general indication whether both PROFCORP and STUCORP contain high frequency signals relating to the Problem-Solution pattern. Appendix 4-1 shows the 100 most frequent words occurring across these four corpora. A striking difference between the composition of the two general corpora and PROFCORP and STUCORP is that the two general corpora mainly consist of function, i.e. grammatical words, whereas content words tend to dominate in the other two corpora. (In the following analyses, the order of frequency is given in brackets for ease of reference in the wordlists).
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Grammatical signals An examination of the wordlists of the core written component of the BNC sampler and COBUILD reveals quite a lot of similarity between the two lists in that most of these items consist of similar function words, which are grammatical in nature. Interestingly, in COBUILD but (20) could be a grammatical signal anticipating a Problem statement, but this could only be verified with reference to the context. Likewise, in the BNC the modal verb should (75), which is often used for proposing solutions and making recommendations, may well signal a Response, but again, we cannot know this without recourse to the context. In these two general corpora, these are the only two items in the 100 most frequent words which hint at the Problem-Solution pattern. Similar grammatical items are also found in PROFCORP and STUCORP: however (72), but (87) and should (55) in STUCORP. A corpus analysis of however (100) from PROFCORP was provided in Chapter 1 to illustrate the type of phraseological analysis that can be undertaken. There are no other potential grammatical signals for the pattern in either corpus. One reason for this could well be that these belong to a closed set of items where few choices are available in the grammatical system, whereas vocabulary items constitute a more open set from which a far greater number of choices are available to express the same meaning. It has already been noted that STUCORP and PROFCORP contain more content than function words in the 100 most frequent words. In the following section, I examine the nature of these content words to determine which ones signal elements of the Problem-Solution pattern, and consequently which kind of lexis they constitute, Inscribed or Evoking, on the one hand, and technical or sub-technical, on the other.
Lexical signals: Inscribed vs. Evoking In STUCORP, there are three Inscribed items: problem (58), need (86) and result (84), which could either be a noun, part of the verb phrase result in, or part of the sentence connector As a result. In PROFCORP the emphasis is clearly on the Solution element, denoted by the following Inscribed items: measures (36), proposed (43), mitigation (45), and recommended (63), with monitoring (49) and assessment (62) for the Evaluation element. As pointed out in Chapter 3 these Vocabulary 3 items could function textually, i.e. as connectives, and also as lexis which operates at a more local level of signalling. However, clearly, frequency lists cannot provide this kind of detailed information.
Chapter 4. Frequency, key word and key-key word analysis
Now I will discuss examples of the other type of lexical signal – Evoking – where the item evokes some kind of evaluation when considered out of context in relation to the reader’s conventional interpretation of it. These Evoking items differ from the Inscribed items described in the previous paragraph as they are the lexical realisations of the Problem statement whereas the Inscribed items are the actual signals for the Problem. Items in PROFCORP which would appear to fit this category include the following: noise (14), traffic (51), waste (53) and dust (72). Impacts (22) and impact (30) are also included here because in the context of environmental studies they imply a negative effect. In the Dictionary of Ecology and Environment (1995) impact assessment is defined as ‘evaluation of the effect upon the environment of a large construction programme’ (p. 122). Disposal (98) is an Evoking item for the Solution element. As mentioned in the previous chapter there also exists another kind of Evoking item where we can only tell from the context whether the word has a positive or negative semantic prosody. In PROFCORP, construction (16), landfill (71) and reclamation (84) are examples of this type of items as they can be regarded as either an element of the Problem or Solution, i.e. they can cause a problem or be put forward as a solution. For example, out of the 356 tokens of landfill, which occurs as a key word in ten reports (see Table 4-2), 125 of these indicate some kind of problem, e.g. …quantities of gas are being generated within the landfill. It is of interest to note that this type of Evoking lexis does not occur in the STUCORP word frequency list. The signals, which are overwhelmingly lexical in nature, are therefore clearly Problem-Solution based, with Inscribed items occurring in both PROFCORP and STUCORP, but Evoking items only found in PROFCORP. A final observation is that in PROFCORP the Inscribed items relate to the Solution whereas the Evoking items tend to focus on the Problem aspect both in terms of a wider range and higher frequency of occurrence.
Lexical signals: Technical vs. sub-technical Apart from this evaluative dimension discussed above from the perspective of Inscribed vs. Evoking items, we can also ask questions about the subject matter of the lexis. An examination of the data points to an interesting intersection of Inscribed and Evoking lexis with sub-technical vocabulary. Following Baker’s (1988: 92) definition of sub-technical as ‘items which express notions general to all or several specialised disciplines, e.g. factor, method and function’, as a general rule, the Inscribed lexis, e.g. problem, need, recommended, tends to be subtechnical vocabulary as such items have a discourse role and are therefore also categorised as Vocabulary 3 items.
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On the other hand, the Evoking lexis highlighted by the frequency analysis of PROFCORP would seem, at first sight, to constitute another type of sub-technical vocabulary. This is because words such as noise, construction, traffic, waste, dust etc., whilst having a high frequency of occurrence within the area of environmental studies, are also used in general English. However, I say at first sight, because in the Dictionary of Ecology and Environment (1995), many of these words are listed as collocating with other nouns and adjectives, thus expressing a concept particular to the field of environmental studies. For instance, ambient noise is defined as ‘= general noise which surrounds an organism (such as traffic noise, waterfalls etc.)’ (p. 10). When a noun in common usage is found to have a strong collocation with a particular noun or adjective in this field, I would argue that it takes on a technical meaning and no longer meets one of the definitions of sub-technical. In other words, ambient noise is therefore a technical term as it does not occur in general English usage and for this reason is to be considered as a multi-word unit rather than a collocation. Another common (technical) collocation in this field is sensitive receiver which is actually defined in one of the reports as ‘A sensitive receiver is a receiver considered sensitive to given impacts from changes in noise or air quality, vibration, land use, visual or landscape impacts’. The above examples illustrate that lexis can change from being sub-technical to technical in nature by virtue of its collocational behaviour (see Williams 1998; Yang 1986, for corpus-based research on technical and sub-technical vocabulary in the fields of biology and engineering, respectively). It is therefore necessary to expand the traditional classification frameworks for technical and sub-technical vocabulary based on single word items to also include multi-word combinations. I will now re-examine some of the Evoking items from the frequency list in light of the above argument with reference to the definitions provided by the Dictionary of Ecology and Environment. The definition for disposal when it collocates with land and marine is given as ‘depositing waste in a hole in the ground’ and ‘depositing waste at sea’. When disposal collocates with refuse, waste or sewage, it has the meaning of ‘getting rid of something’. (p. 71). In addition to ambient noise (general noise surrounding an organism), I would argue that all these items of Evoking lexis are technical as they would not commonly be found in general English. Using the Concord tool in Wordsmith would verify whether, for example, noise, if it collocates with ambient should be regarded as technical or sub-technical. This point will be taken up in Chapter 6. The same case can be made for Inscribed items, e.g. Impact Assessment (evaluation of the effect upon the environment of a large construction programme) for the Problem element and Mitigation Measures (measures taken to offset these negative effects) for the Solution element. In fact, both these collocations are of-
Chapter 4. Frequency, key word and key-key word analysis
ten used as sub-headings in the environmental audit reports, which denotes the discourse-organising status of this Inscribed lexis. The foregoing discussion has shown that in STUCORP there are a few Inscribed items such as problem and need which could be categorised as sub-technical vocabulary. On the other hand, PROFCORP contains a higher proportion of Evoking items such as noise, construction, and traffic, which, taken as they stand, are also sub-technical but could also be classified as technical by virtue of their collocational behaviour. However, this point still needs to be verified. This analysis may therefore challenge the assumption that lexical signals are always sub-technical vocabulary, but more data on collocational patterning is needed before this can be accepted. Notwithstanding, preliminary examination of these frequency lists has thrown up some interesting data for more in-depth analysis in subsequent chapters. These preliminary data do suggest that both PROFCORP and STUCORP differ from the two general corpora in their fundamental makeup by virtue of the high frequency of several lexical items of the Problem-Solution pattern, although it has been noted that these are far more prevalent in PROFCORP, especially of the Evoking type. I have also proposed a redefinition of the notion of technical vs. sub-technical vocabulary to take into account collocational patterning. This point will be revisited and explored in greater detail in Chapters 6 and 7. However, such type of frequency data only gives us a very general overview. A key word analysis, by revealing words of unusually high frequency as outlined briefly in Chapter 1, would provide more insights into the genre or discourse patterns of the corpus.
Key word analysis of signals Scott’s (1997) starting point is with the concept of “aboutness” (Phillips 1989); i.e. the content of the text, which relates to Halliday’s (1994) ideational metafunction. Moreover, like Hoey (2001) he also recognises that a text’s “aboutness” depends on the reader’s decoding of the text. ‘Aboutness is a function of a textin-the-world: that is, it needs a human reader or listener to perceive it, to decide what it is. The text alone is only potentially “about” something’ (Scott 2000a: 107). He then goes on to ask where is this “aboutness” located in the text and how is it signalled internally. He answers this by offering the concept of “keyness”, ‘a word which occurs with unusual frequency in a given text’ (In fact, a key word can be either positive, i.e. of unusually high frequency, or negative, of unusually low frequency). The computational procedure for identifying a key word is a purely mechanical procedure which does not rely on a knowledge of English or world-
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knowledge: the identification of key words is through a comparison of the repetition of word-types with word-tokens (Scott 1996–1999). This key word procedure has been used for uncovering stylistic features (Scott & Tribble 2006), identifying the genre to which a text belongs (Tribble 2002) and revealing sub-technical and non-technical vocabulary in engineering texts (Mudraya 2005). However, to my knowledge, apart from a small-scale study by Scott (2001b), no large-scale computational analysis of signals for elements of the Problem-Solution pattern has been undertaken to date. I will now examine the key words in each corpus as a whole and then in each separate report to determine whether these display lexical signals or lexical realisations for the pattern. My hypothesis would be that the key words in each text file, i.e. report, would contain signals for elements of the pattern, thereby providing further evidence for classifying these reports as Problem-Solution based because the pattern itself was so salient and the link between particular words and roles the pattern so fixed. For the reference corpus, I used the one-million word core written component of the BNC, as I did not have access to the full BNC at the time. However, this general corpus is still satisfactory for my purposes as it contains 86 texts from nine different subject domains. But it is well to bear in mind that ‘Times Change, and so do Corpora’ (to borrow a title from Johansson 1991). With the growing importance attached to the role of ‘International English’ or ELF, English as a Lingua Franca, in corpus-based studies (see Mauranen 2003; Seidlhofer 2001), it is expected that future research will draw on the localized data of the International Corpus of English, such as ICE–HK, for exploitation as suitable contrast corpora (see http://www.hku.hk/english/research/icehk/index.htm). (I do not regard PROFCORP as having the full status of international English because of the socio-cultural conditions in which the reports were constructed (see Chapter 3) and for this reason PROFCORP may have more in common with the variety of English in the ICE-GB component). In addition to the choice and size of reference corpus, another consideration when using the key words procedure is the cut-off point for the Log Likelihood which is set by establishing a minimum significance, i.e. p value; the smaller the p value the fewer key words in the display. For computing the keyness, the p value was set at 0.000000000001 to obtain a reasonable number of key words for analysis and the minimum frequency requirement was left at the default value of 3 to avoid being swamped by data. The number of key words in each report ranged from 15 to around 90 (in the Help files, Scott, in fact, suggests having around 40 key words as a reasonable number on which to draw conclusions about a text). The following section summarises the main findings according to the categories of Inscribed vs. Evoking lexis with reference to its status as technical vs. subtechnical vocabulary. These key word results will also be compared with those
Chapter 4. Frequency, key word and key-key word analysis
of the frequency analysis in the previous section. (As grammatical items did not show up in the key word analysis, as mentioned in Chapter 1, they are not dealt with here).
Lexical signals: Inscribed vs. Evoking The list of key words in each corpus and in each individual report was also examined for instances of Inscribed and Evoking items for the Problem-Solution pattern. In PROFCORP a distinct pattern began to emerge across the corpus as a whole and all the reports. In the whole PROFCORP corpus 15 out of the top 40 key words can be classified as belonging to one of the categories for the pattern. The Evoking items, noise, impacts, impact, waste, traffic, dust, realise the Problem element with construction, landfill and reclamation having the potential to be either the Problem or Solution element depending on the context. In contrast, the Solution element is signalled by key words of an Inscribed nature, e.g. mitigation, measures, proposed, recommended, with monitoring and assessment used for the Evaluation element. These findings are very much in keeping with the findings from the frequency analysis and also mirror the same kind of patterning as that found in the individual reports. Another important observation is that in every single report in PROFCORP there were two or more Inscribed signals and four or more Evoking items which clearly shows that not only the corpus taken as a whole but also each report can be considered as Problem-Solution based. Appendix 4-2 presents the key words from a PROFCORP report to illustrate this finding. In contrast, out of the top 40 key words in STUCORP there is only one Inscribed signal, i.e. problem. Furthermore, 22 out of the 80 individual reports in STUCORP do not contain any key words which are obvious as either Inscribed or Evoking items for the Problem or Solution elements. However, in the remaining 58 reports there is a much wider range of Inscribed signals for both the Problem and Solution element than was noted in the frequency analysis, which can partly be explained by the default minimum of three. For example, we find the following superordinate words, including the signal problem, which all imply some kind of problem statement: concern, need, failures, burden, difficulties, dissatisfaction, dishonesty, destruction, shortcoming, issue, demand. It is interesting to note that some Inscribed items are also adjectival in nature, e.g. insufficient, inadequate, inefficient, unfair, uninteresting, unsatisfied, where the negative import of the word is signalled by the prefix -in or -un. These findings are in contrast to both the keyword analysis of the whole corpus and also the frequency analysis, where the only Inscribed items noted for the Problem element were problem and
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need. It is unlikely that the above key word items would show up in the 100 most frequent words or as key in the corpus as a whole as they only occur as key in one, two or three out of the 60 reports. We also have a similar situation regarding the Inscribed items for the Solution element, with solution, solutions, proposal, proposed, suggestions, suggested, recommendations, recommended, occurring as key in only a handful of the reports. Evoking key words, e.g. cheating, rubbish, accidents, crimes, theft, infringement, debts, penalty, overcrowding, stress, were also found in the individual reports, but did not show up either in the frequency analysis or in the key word analysis of the whole corpus.
Lexical signals: Technical vs. sub-technical The key word list of each report in PROFCORP was trawled through manually to isolate instances of technical vocabulary occurring as key. It was found that there were only seven reports which did not contain any technical vocabulary amongst their key words. A most interesting finding was that 50 out of the 60 reports contained abbreviations which could be regarded as standing for ‘technical phrases’, or multi-word combinations to use Yang’s (1986) terminology. Appendix 4-4 presents a list of the technical vocabulary occurring either as individual key words, or key phrases signalled by the use of abbreviations. As can be seen from this list, the majority of technical vocabulary appears in abbreviation form, which I have attempted to classify under subject-related headings. There is one term under section H which deserves special mention here. In several reports there are allusions to Fung Shui (i.e. geomancy) matters, which are “of particular concern to villagers” and “are sensitive issues” as they play a part in the decision-making of where construction should be carried out. While Fung Shui is not strictly a ‘technical’ term, I have included it in this list as it demonstrates how cultural issues can affect technical operations, as exemplified by the following extract: Proposed offshore berths are located to the northeast of Sha Chau, therefore avoiding direct intrusion to the “Fung Shui” of the temple which has a northwest aspect.
The remainder of the technical lexis is in the form of multi-word combinations transcribed as abbreviations after the initial mention of the item. As I have suggested earlier, many of the individual words in these abbreviations could be viewed as sub-technical, e.g. facility (see Appendix 4-1), as they have a high frequency of occurrence in this field but are also used in general language. However, when these words combine with other words, the whole expression takes on a technical
Chapter 4. Frequency, key word and key-key word analysis
aspect as it would not be found in general English, e.g. Refuse Transfer Facilities, RTF (see section F in Appendix 4-4). Moreover, the use of abbreviations for these multi-word combinations strengthens the case that they should be regarded as fixed expressions, i.e. as one conceptual entity having a technical meaning in this particular field. I will now examine whether these key technical multi-word combinations bear any relation to the Inscribed and Evoking key words for the Problem-Solution pattern noted in the previous section. The Inscribed signal assessment combines with impact to form Impact Assessment, which is a common key word term in section A ‘Environmental Study’. As for the Evoking items, it was noted earlier that key lexis such as noise, traffic, discharge and waste carries a negative connotation which would therefore signal the Problem element. However, when we examine the list in Appendix 4-4 we find that noise and waste occur in abbreviated phrases which signal the Solution element – see for example, Allowable Noise Levels (ANL) and Noise Control Ordinance (NCO) in section B ‘Environmental rules and regulations’ and Low-level Radioactive Waste Storage Facility (LRWF) in section F ‘Mitigation Measures’. Such abbreviations could be regarded as a condensed form encapsulating both problem and solution elements; for example, ‘noise control ordinance’ can be paraphrased as an ordinance in order to control noise, using a two-way signaling verb (see Chapter 1). Moreover, prosodic meaning may be, but is not necessarily accessible via conscious reflection. In cases where native-speaker intuition proves unreliable, corpus data can be of use in uncovering this prosodic meaning, as in the case of landfill. An examination of the corpus co-text reveals landfill to have a negative meaning when it combines with gas, i.e. Landfill Gas (LFG) in section E ‘Gases / Metals causing environmental damage’, but a positive meaning in Pillar Point Valley Landfill (PPVL) in section F ‘Mitigation Measures’. An examination of the list of key technical vocabulary in STUCORP (see Appendix 4-5) shows that several of these items are computer-related, although some would argue that lexis such as Internet, e-mail and PC, like kg. and km. in section G ‘Measurements’ in Appendix 4-4, are sub-technical rather than purely technical as they are found in general English. However, I view them as technical as they retain the same meaning in both general and more specialised usage, which conforms with Goodman and Payne’s (1981) definition of technical terms having congruity among scientists (unlike the term ‘cell’, for example, which has a different meaning in biology to that in general English). Here, we have an example of de-terminologization which refers to a process whereby specialist terms such as those relating to computers make their way into general language through the mass media or direct impact (Bowker & Pearson 2002). I have also included university departments and service centres in the list in Appendix 4-5 for the rea-
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son that their abbreviated forms, which occur as key words, would not be found in general language. It is evident that this list is quite short, most probably because in the guidelines for this assignment students are instructed to explain or couch technical information in language that is accessible to a manager who may not be a specialist in the field (see Chapter 2). Unlike in PROFCORP, none of these technical words relate to the Problem-Solution pattern and there is no overlap between these technical key words and the Inscribed and Evoking items discussed previously. If we now return to Appendices 4-2 and 4-3, which present the key word lists for one PROFCORP and one STUCORP report, it is evident that in general these lists consist of a mixture of technical and sub-technical vocabulary. The sub-technical vocabulary comprises discourse-organising words, such as measures and recommended in Appendix 4-2, and problems and need in 4-3, and the technical vocabulary largely by abbreviated terms in the PROFCORP reports (and possibly by collocations although this has yet to be proved), and computer-related terms, e.g. server, dial-in in the STUCORP reports. The prevalence of abbreviations showing up as key words of a technical nature, especially in PROFCORP, suggests that the use of abbreviations in scientific writing is an area that merits attention for future research. Having considered these vocabulary issues, i.e. the intersection of Inscribed and Evoking items with technical and sub-technical vocabulary, in relation to the corpus as a whole and to individual texts, I will now examine their keyness across a number of texts.
Key-key word analysis of signals Whereas the key word analysis can tell us which words are key in a given text, the key-key word analysis goes one step further by showing the words which are key in a large number of texts. This can be done by creating a database from the key words files, which will list the key words which are most frequent over a number of files. The key words technique is very useful in revealing the genre or discourse characteristics of the corpus as a whole, through a set of semantically-related key words as mentioned previously, but the key-key word analysis gives a more delicate analysis by showing the number of texts in which the word is found to be key, i.e. a word’s “keyness”. For example, each of the 60 reports and 80 reports in PROFCORP and STUCORP respectively will have its own key words. These key words will probably fall into two main categories. There will be key words which are key in one report, but not generally key in others. In the case of PROFCORP, where the reports come
Chapter 4. Frequency, key word and key-key word analysis
from various companies working on different projects for different areas in Hong Kong, we would expect the names of the companies (e.g. Maunsell) and areas (e.g. Wan Chai) to be key in a restricted number of reports. The other category of key words would consist of more general lexis which would be typical of the discourse under investigation, and thus be classed as “key-key” words. It is hypothesised that words relating to the Problem-Solution pattern would occur as “key key” lexis in STUCORP and PROFCORP. Appendices 4-6 and 4-7 show a list of key-key words occurring in 4 or more texts in PROFCORP and STUCORP respectively. Four texts were chosen as the cut-off point as those key words occurring in three texts or below commonly related to technical vocabulary, e.g. dot-matrix in STUCORP or the names of companies, e.g. Maunsell in PROFCORP, which did not relate to elements of the Problem-Solution pattern.
Lexical signals: Inscribed vs. Evoking Table 4-1 (based on the statistically prominent words extracted from Appendices 4-6 and 4-7) shows the Inscribed signals from each corpus with the number of texts in which they are key noted in brackets. However, as mentioned previously, at this stage of the analysis, we do not yet know whether these Inscribed signals
Table 4-1. Inscribed signals in STUCORP and PROFCORP Element
STUCORP
Problem
Problem (8) Problems (4) Need (4) Insufficient (5) Recommendations (5) Solutions (4) Solution (4)
Solution
Evaluation
Feasibility (8) Feasible (6)
PROFCORP
Mitigation (43) measures (30) Proposed (28) Recommended (27) Recommendations (8) Treatment (9) Options (5); Plan (5); Scheme (5) Minimise (5) reduce (4) ensure (4) Monitoring (29) Assessment (23) Audit (5)
Note. Figures in parentheses denote the number of texts in which the words were found to be key.
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46 Corpus-based Analyses of Problem-Solution Pattern
are functioning at a textual or local level of coherence. (This aspect will be covered in Chapters 6 and 7.) While the above table gives clear proof that both corpora comprise ProblemSolution based reports, their overall profiles of the patterning are somewhat different. These differences will be discussed in detail in the following main section. In PROFCORP, Concord, another tool in WordSmith, was used to examine certain words at sentence level, namely options, plan and scheme, to determine whether they qualified as Inscribed signals, and if so, for what elements of the Problem-Solution pattern. One point uncovered by this analysis was the function in PROFCORP of seemingly synonymous lexis. (This issue of under what circumstances one word is used in preference to another with the same semantic equivalence was raised in Chapter 1.) For instance, design, project, scheme and plan are all listed as synonyms in the Collins Bank of English Thesaurus (1998), but in the context of the environmental reports in PROFCORP, one cannot necessarily be substituted for another. For example, design and project are always used to designate some type of construction in the Situation element and they occur as key words across twelve and eight reports, respectively. Even though both words are used for the Situation, the following examples show that they are not interchangeable: At present, the programme for design of the sewerage along Castle Peak Road is not determined and the pipe sizes and invert levels have yet to be decided. Certain amendments to the construction specification have also been found necessary and have been accepted by tenderers for the project.
In contrast, plan is never used to mark the Situation, but always occurs as key lexis for the Solution element, signalling a solution for some kind of environmental effect: Contractually, a noise limit together with a noise monitoring and action plan can be specified in the contract to control noise.
Like plan, the items, scheme and proposed also signal a proposed solution, as in the two examples below: Other benefits of the Stage 1 Scheme include: the removal of pollution loads from water bodies in the Eastern and New Territories. Fifteen measures were proposed to avoid or mitigate air quality impacts
The above examples thus highlight the dangers of relying solely on a thesaurus for clarification of meaning as contextual issues can also come into play.
Chapter 4. Frequency, key word and key-key word analysis
Table 4-2. Evoking items in STUCORP and PROFCORP Element
STUCORP
PROFCORP
Problem
stolen (4)
Impacts (50) impact (26) Contaminated (14) contamination (4) Noise (44) traffic (23) Sewage (12) sewerage (6) Waste (20) wastes (5) dust (20) Pollution (10) emissions (10) Sediments (10) odour (9) Effluent (6) discharge (5) Discharges (5) NSRS (9) Dba (8) TSP (7) leachate (6) stormwater (5) groundwater (4) * landfill (10) * construction (47) Disposal (14) implementation (6) Barriers (5) Ordinance (4) * Landfill (10) * construction (47)
Solution
Note. Figures in parentheses denote the number of texts in which the words were found to be key. Italics = technical vocabulary. * Can signal either the problem or the solution element.
It was not surprising to find feasibility and feasible in STUCORP, and monitoring, assessment and audit in PROFCORP acting as Inscribed key-key word signals for the Evaluation element given the context of writing. In STUCORP the Inscribed lexis is used for assessing the practical implementation of the Solution before it has been introduced, (i.e. is it feasible to implement the proposed solution from a technical, economic, environmental and social point of view), whereas in PROFCORP the recommended monitoring, assessment and audit measures are applied to the Solution when it is already in place. The list of Evoking items in Table 4-2 is valuable in that, like the list for the Inscribed signals, it gives us a general profile of the subject matter and the textual patterning of each corpus. There exist quite striking differences between the two corpora, which will also be taken up in the following main section. In PROFCORP, there were problematic areas in the classification where it was necessary to use the Concord software in order to determine how certain Evoking items should be categorised. Construction and landfill are classified under both the Problem and Solution as Concord shows that they are used in different
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48 Corpus-based Analyses of Problem-Solution Pattern
situations. As pointed out previously, sometimes it is only from the context that we can tell whether an item evokes a positive or negative semantic prosody. For example, landfill can refer to a proposed solution, as signalled by … it is proposed … in the example below: In order to provide a continuous waste disposal outlet, it is proposed that 3.2 million cu metres of landfill space should be provided.
Later, in the same text, though, this proposed solution is seen as generating a problem, signalled by the use of impacts:
The major impacts due to waste deposition at the landfill will be additional leachate and landfill gas that would be generated.
Here, we have an example of what Hoey terms ‘progressive multilayering’ where the Response, i.e. to build a landfill site, only solves part of the problem as it sets up another problem to be solved, i.e. the management of leachate and landfill gas (see Chapter 1).
Lexical signals: Technical vs. sub-technical The Inscribed and Evoking signals also interface with the categories of technical and sub-technical vocabulary. The majority of the Inscribed signals in Table 4-1 can be classified as sub-technical vocabulary as they have a discourse-organising function. However, I have also made the point that in cases where certain lexis collocates to form fixed phrases, these should be regarded as technical vocabulary as they are specialised terms in the field and do not occur in general language even though each separate word of a combination might occur in general English. Impact Assessment and Mitigation measures are examples of such lexis. From Table 4-1 it can be seen that the lexis in these two phrases occurs as key in a large number of reports, thereby delineating these words as the main superordinate lexis for the Problem and Solution elements of the pattern. None of the technical vocabulary in STUCORP (see Appendix 4-5) relates to the Problem-Solution pattern, whereas many of the items in PROFCORP (see Appendix 4-4) do. In Table 4-2 the technical vocabulary in PROFCORP is indicated in italics and always occurs in the Problem slot. As pointed out earlier, at first glance, the remaining words would be classified as sub-technical according to my definition, as they are also used in general English. However, if we compare these Evoking items with the technical vocabulary listed in Appendix 4-4, we find a considerable overlap between the individual items in Table 4-2 and their occurrence as part of an abbreviated term. For example, waste, noise, sewerage and ordinance are found under section B ‘Environmental rules and regulations’ in
Chapter 4. Frequency, key word and key-key word analysis
Appendix 4-4. Another observation is that whereas sewage and leachate have been categorised as Problem in Table 4-2 on the basis of their inherent negative connotation, they fall under F ‘Mitigation Measures’ in Appendix 4-4 (Sewage Treatment Works, STW, and Leachate Treatment Facility, LTF) on account of their combination with other lexis. Further investigation at sentence level is therefore necessary to shed light on under what circumstances such lexis should be classified as technical or sub-technical. It is to be noted from Appendix 4-4 that a substantial proportion of the technical vocabulary in PROFCORP only occurs as key in one or two reports and is therefore not recorded in Tables 4-1 and 4-2. One might assume from the data that this technical vocabulary is particular to a handful of reports which could be from the same company. However, this was found not to be the case with the majority of the low frequency key-key word terms. Concord was used to determine from which file the vocabulary was extracted. For example, AQO (Air Quality Objectives) in section B ‘Environmental rules and regulations’ in Appendix 4-4 is only found to be key in two reports, but in fact the 49 occurrences of this term are spread over 6 different reports which are from over half of the major companies supplying these reports. This technical vocabulary can therefore be considered as fairly representative of the field on the whole although it might only occur as key in a few reports.
Differences between PROFCORP and STUCORP In general, the key words and key-key words of Inscribed and Evoking items mirror the pattern uncovered in the first stage of the analysis, i.e. the frequency analysis, thus providing ample evidence for designating both corpora as Problem-Solution based. As for the Inscribed items, these were found in both STUCORP and PROFCORP with a sole focus on the Solution element in PROFCORP, as shown in Table 4-1. By contrast, a range of Evoking items were found in PROFCORP for both elements of the pattern with a focus on the Problem element. IN STUCORP, though, no Evoking items were present for the Solution element and only one for the Problem element (please refer to Table 4-2). These differences can be accounted for by both the type of task and the apprenticeship nature of the Student Corpus, as discussed below. One of the main reasons for this discrepancy lies in the composition of the two corpora. The PROFCORP reports are relatively homogeneous in terms of subject matter, as they are all environmental audit reports and we would expect the same Inscribed and Evoking lexis to occur across reports, which has indeed been shown to be the case. In contrast, the STUCORP reports cover several dif-
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Corpus-based Analyses of Problem-Solution Pattern
ferent topic areas, e.g. computer facilities, sports programmes, language courses, credit card usage, academic dishonesty, laboratory security (see Appendix 2-2). The key word vocabulary is therefore much more diffuse, as reflected by the wider range of topics, which means fewer occurrences of key words across reports. However, some of the differences could also be accounted for by the linguistic competence of professional compared with less experienced or apprentice writers, as evidenced by the limitations of vocabulary in STUCORP for expressing the Problem-Solution pattern. Whereas the pattern is represented multi-lexically in PROFCORP, i.e. through Inscribed (which overlaps with sub-technical), Evoking and technical lexis, it is represented almost exclusively uni-lexically in STUCORP, i.e. only through Inscribed signals. Tables 4-1 and 4-2 show that the reports in STUCORP employ the basic metalanguage of the pattern, which can, in part, probably be traced back to the rubrics for the assignment (see Chapter 3 and Appendix 3-2). These stipulate that students are required to investigate a problem or need, propose solutions and evaluate the feasibility of implementing these solutions. It appears, therefore, that students are incorporating this metalanguage provided in the assignment guidelines into their project reports, with problem being a salient key-key word. (Here, I use ‘salient’ in the sense of ‘an important feature to note’, although problem is much less statistically prominent than some of the other key-key words in Table 4-1). In fact, problem and need were also found to occur in the 100 most frequent words in STUCORP and problem was noted as the only word relating to the pattern which occurred as key in the corpus taken as a whole. And how can we explain the paucity of key-key word Evoking items in STUCORP? There is only one Evoking item for the Problem element in STUCORP i.e. stolen, whereas in PROFCORP there are 26 items which are with one exception all nouns One possible reason is that students are operating within a narrow lexical range and may tend to fall back on using the superordinate category of Inscribed signals such as problem because they lack a more sophisticated repertoire of Evoking lexis for realising the Problem and Solution elements. Another explanation, though, is that as the topics of the student reports cover a much wider subject range, and consequently would have their own Evoking lexis, the same vocabulary items would not occur across reports and therefore would not show up as key in four or more of the reports. Nevertheless, there are 8,724 different types in PROFCORP with 7,268 in STUCORP, suggesting that students may be over-relying on the metalanguage exposed by the keyword Inscribed signals. Chapters 6 and 7 shed more light on this issue in the examination of phraseologies for selected key words.
Chapter 4. Frequency, key word and key-key word analysis
Conclusion This chapter has identified the high frequency, key word and key-key word lexis in PROFCORP and STUCORP and demonstrated, via internal linguistic criteria, that both corpora can indeed be classified as Problem-Solution oriented. In fact, the tabulated findings can be regarded as a form of outline of the rhetorical structure of the reports. For example, the Inscribed signals in Table 4-1 constitute the outline format for STUCORP, whereas in PROFCORP the outline for the pattern is mainly highlighted by the Evoking items for the Problem, but Inscribed lexis for the Solution element (see Tables 4-1 and 4-2). The Problem-Solution pattern is further reinforced in PROFCORP by the key technical vocabulary presented in Appendix 4-4. For instance, the technical items under section E ‘Gases / metals causing environmental damage’ and under sections B ‘Environmental rules and regulations’ and F ‘Mitigation measures’ signal the Problem and Solution elements, respectively, which intersect with the Inscribed and Evoking items in this corpus. It has also been put forward that the linguistic evidence drawn from these two corpora can (tentatively) lead us to distinguish experienced writers from less experienced or apprentice writers on account of the narrow range and paucity of Inscribed and Evoking items in STUCORP, although more evidence from a phraseological perspective is needed to substantiate this observation. This is the focus of the following chapters.
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chapter 5
PROFCORP Phraseological analysis of signals for the Problem element
In Chapter 4 I identified the Inscribed and Evoking keywords for the Problem-Solution pattern in PROFCORP and STUCORP (Tables 4-1 and 4-2). In this chapter, I analyse selected items for the Problem element in PROFCORP from the perspective of two broad categories: – Causal semantic relations and non-causal phrases – Lexico-grammatical patterns Examining lexical items realising the Problem-Solution elements within a framework of semantic relations will set the phraseological analysis at a more discoursebased level, an overall aim which was stated in Chapter 2. I also rely on other Hallidayan categories, namely Theme/Rheme patterning to shed light on the data, and also look at whether recurrent patternings can reveal the discursive practices of this quite conventionalised written genre of EIA reports, also mentioned as broad aims in Chapter 2. A more detailed overview of these two strands of the classificatory framework, together with some preliminary brief examples from PROFCORP to illustrate the various investigative procedures, is outlined below. I also use the Applied Science component of the BNC (approximately 7 million words; see Aston & Burnard 1998; Burnard 2002), which is the closest large-scale reference corpus in terms of subject matter to the EIA specialised corpus, in order to substantiate some of the findings.
Classificatory framework: Causal semantic relations In spite of Swales’ reservations about corpus-based methodologies reviewed in Chapter 2, this proposed framework is an attempt to go some way towards achieving a ‘symbiosis’ between more ‘top-down’ and ‘bottom-up’ approaches.
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Corpus-based Analyses of Problem-Solution Pattern
The brief corpus-based analyses in Chapter 1 provided preliminary evidence that problem statements are commonly found in some type of causal clause relation. Halliday and Hasan (1976: 256–261) discuss three specific types of causal relation (Reason, Result and Purpose) and also Conditional relations under the general causal relation. These all belong to Halliday and Hasan’s conjunctive relation, one of the four types of relations for creating cohesion in text. A similar framework for signalling general semantic relations of cause–effect has been put forward by Crombie (1985), who proposes the following specific categories explicated below:
Reason – Result Means – Result Grounds – Conclusion Means – Purpose Condition – Consequence
B the result of A B by means of A B deduced from A A in order to B B would result if A
Some corpus-based work has already been carried out using the above semantic framework (Flowerdew 1998b) comparing various explicit linguistic devices for expressing the three semantic relations of reason – result; means – result; and grounds – conclusion in a 40,000 word corpus of undergraduate academic writing with a comparable corpus of expert writing, Global Warming: The Greenpeace Report, one of the mini-corpora in the MicroConcord Academic Corpus Collection. Marco (1999) also touches on this area in her corpus-based research on the lexical signalling of, what she terms, conceptual relations. She notes that the cause-result relation is often realised by the nominal phrase the result of. Although I am proposing to use a similar classificatory framework to the one in my 1998b study, my focus is different. In this study, I am concerned with how the signals for the Problem-Solution pattern operate lexico-grammatically within this framework rather than simply looking at how these relations are realised linguistically. The lexico-grammatical patterning for these relations is analysed, but in relation to the signals within the various semantic categories. Below I give an example of some typical phrases for the signal problem in PROFCORP for Crombie’s five categories within the general semantic relation of cause-effect.
Reason – Result Means – Result Grounds – Conclusion Means – Purpose Condition – Consequence
… export scheme will create a noise problem. … thereby averting an odour problem … and so flooding is not a serious problem. …in order to alleviate the problem of.. … If there is a problem with …
It can therefore be seen that, at the highest level, I am adopting a notional (also sometimes referred to as ‘conceptual’) classificatory framework for the phrases in
Chapter 5. PROFCORP: Problem element
which the signals for the Problem element occur. Those phrases which cannot be assigned to one of the causal categories above will be analysed according to their function in the discourse. The following sub-section will outline the procedures for delineating the lexico-grammatical patterning of this Inscribed and Evoking vocabulary.
Classificatory framework: Lexico-grammatical patterns In the corpus-based approach, one key issue is whether the point of entry to investigation is with the pattern grammar or the lexis. Sinclair’s work is based on possibly two contradictory methodologies. One involves the researcher painstakingly investigating the phraseology of one lexical item after another. The other involves the use of a computer to list the most frequently-occurring word sequences. Francis faces the same problem of whether to take the lexical item as the starting point or whether to take the patterns as the starting point. She investigates the adjective, possible, for example, and notes that it occurs with an unusually wide range of patterns, each of which it shares with other adjectives. On the other hand, she investigates patterns such as the appositive that-clause, and lists the nouns which share that pattern. (Hunston & Francis 2000: 31)
Whether the pattern grammar or the lexis is used as an entry point would very much seem to depend on the purpose and scope of the investigation and also the nature of the corpus. For compiling a comprehensive lexico-grammar of the English language it may be best to start with the pattern and identify all the words that have a particular pattern. However, where the aim is to examine the lexicogrammar in a specialized corpus, it may be more opportune to start with the lexis. As Sinclair (2005) notes ‘the characteristic vocabulary of the special area is prominently featured in the vocabulary lists’ (see Chapter 3). This is indeed what the analyses in Chapter 4 have revealed, the key lexis for defining the ProblemSolution pattern, thus providing a justification for using the lexis as a starting point for analysis. At the primary level of delicacy I first examine the collocational patterning of selected items for the Problem element. In Chapter 1 the concordance for pollution showed it to collocate with ‘minimise’. Another way of looking at collocation is to start with the verb and then move to the noun, which then raises the question of a word’s semantic prosody. As Stubbs (2001b) points out, CAUSE collocates with words with unpleasant connotations, e.g. problem, damage, death, disease, whereas PROVIDE collocates with words with positive semantic prosody such
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Corpus-based Analyses of Problem-Solution Pattern
as aid, care, employment and facilities. In contrast to Stubbs, my starting point is with the lexical item working back to investigate which verbs an item typically collocates with. This procedure has the advantage of throwing up a range of verbs which collocate with these evaluative items. For example, in PROFCORP, problem not only collocates with the verb cause*, but also create*, present* and pose*. Concordancing separately on these verbs would show whether they tend to be associated with a negative or positive semantic prosody. By way of a brief example, using the core written component of the BNC, I found, like Stubbs, that cause* overwhelmingly collocates with nouns with a negative semantic prosody, and also often occurs in the syntactic structure verb + adjective + noun (e.g. … would cause considerable water damage …). Where pose* and present* have the meaning of cause, the 15 and 16 examples respectively are all associated with nouns with a negative connotation. However, pose* and present* are unlike cause* in two respects. First, they usually collocate with nouns such as problem, difficulties, danger, which are Inscribed signals for the Problem element, whereas cause* is more likely to occur with Evoking nouns (e.g. your exhibition is likely to cause traffic congestion …). A unique feature of present*, however, is that in seven out of the 16 occurrences present* is used with a noun whose negative import is neutralised, e.g. This will present no difficulties during your holiday. Create*, on the other hand, is used more often with words with pleasant associations, e.g. Water bubbles up through the pebbles, creating a cool refreshing effect. This brief analysis of causative verbs shows that words seemingly belonging to the same semantic set do, in fact, have different collocational behaviours not only in terms of their semantic prosodies, but also semantic preferences for either Inscribed or Evoking items (semantic preference is usually used to refer to the nature of the noun, whether it be concrete or abstract, or belonging to a certain semantic field, e.g. disease etc.; see Partington 2004). Another objective is to investigate the grammatical company that a word keeps (see Chapter 1). With respect to the collocates of the item under investigation, we could also consider the grammatical company that a collocation keeps, which could be viewed as lexical colligation. For example, it has been noted that in PROFCORP problem collocates with the verbs cause*, create*, present* and pose*. The next step would be to consider the tense, voice and aspect, i.e. the colligational preferences, of the verbs in these verb + noun collocations. Apart from the usual features considered in a phraseological approach, i.e. collocation, colligation, semantic prosody and semantic preference, another consideration relates to the interpersonal dimension. It is also important to know whether the main verb is marked interpersonally either with a modal verb or some other expression such as unlikely or probably to indicate epistemic use. Another point of interest is whether different forms of a lemma, e.g. problem and
Chapter 5. PROFCORP: Problem element
problems pattern differently, not only in terms of their lexico-grammatical patterning but also in terms of their distribution across the five causal categories and in any non-causal phrases. It may also be the case that certain phrases are associated with a particular Theme/Rheme position in the clause or sentence within a particular causal category. All these aspects will be considered in the analyses below of selected items from PROFCORP: the Inscribed signals problem, problems, need, and the Evoking items impact and impacts.
Analysis of problem and problems The Inscribed items problem and problems do not occur as key-key words in PROFCORP (see Table 4-1). However, as problem and problems occurred as keykey words in STUCORP, it was decided to examine these in PROFCORP as a point of comparison. Table 5-1 below presents a summary of the findings for problem and problems in PROFCORP, based on the classificatory framework outlined in the previous section. The tokens in each causal category refer to the number of tokens in the text; the tokens where either problem or problems occur as a heading or sub-heading have been excluded from the table below.
Causal categories for problem It can be seen from the above table that the majority (29) of the 41 tokens for problem in PROFCORP fall into the category of Reason-Result, which can be Table 5-1. In-text tokens for problem and problems in PROFCORP PROFCORP (SUB)-HEADING CAUSAL RELATION Reason-Result Means-Result Grounds-Conclusion Means-Purpose Condition-Consequence Total (causal) Non-causal Overall Total (In-text)
Problem
Problems
0
0
29 2 1 6 1 39 2 41
20 0 3 10 2 35 16 51
57
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Corpus-based Analyses of Problem-Solution Pattern
viewed either from the perspective of cause/reason, i.e. what problem is caused, or result/effect, i.e. what is the cause of the problem. However, under Reason-Result, I also consider what is offered as the solution to a potential problem as this is also a type of causation as explained below. Out of the 29 tokens of problem in a Reason-Result relation, 25 of these can be classified as cause/reason, i.e. what problem is caused. This concept is overwhelmingly expressed via explicit and implicit causative verbs rather than through other linguistic devices such as complex prepositions [e.g. due to (1), because of (1) or nouns, e.g. cause (1)]. The explicit causative verbs collocating with problem are create (4), cause (2), pose (2), present (1), become (1). These verbs are invariably in the active voice with a variety of modal auxiliaries used to indicate a possible future problem arising, e.g.: …works at the tunnel portal will create a noise problem.
There are six tokens of problem which occur with implicit causative verbs (minimise, alleviate, eliminate, avert, resolve, address). Implict causative verbs are defined by Fang and Kennedy (1992: 65) as ‘those which entail the meaning of ‘…make somebody/thing do something’ or ‘make somebody/thing + adj.’ The five verbs in this context can all be roughly paraphrased as ‘make the problem better’ in some way. For example, minimise in the phrase …should minimise much of the problem, can be paraphrased as ‘make [the problem] less severe’. In one case the construction ‘adverb + ing’ was used, e.g. …thereby averting an odour problem. These data thus reveal that when problem collocates with explicit causative verbs, these have a negative semantic prosody. However, when problem collocates with implicit causative verbs, these take on a positive semantic prosody (see Louw 1993; Stubbs 2001c), acting as a two-way signal for the Problem-Solution pattern (Hoey 1983). Problem was also found to collocate with various parts of the verb ‘be’ in six phrases. Here, ‘be a problem’ is found to combine with some of the technical keyword Evoking lexis for the Problem element, e.g. leachate, listed in Table 4-2. Reference is made to a specific existing problem in the Theme part of the sentence, which constitutes the ‘point of the departure of the message’ (Halliday 1994). Problem always occupies Rheme position, i.e. the rest of the message. I would like to argue that in the context of these environmental reports ‘be’ takes on the semantics of a causative verb rather than acting as a stative verb, as it implicitly means that a present problem could create a future one. To illustrate, ‘be’ could well be substituted by an explicit causative verb, such as ‘pose’ or ‘present’ in all the examples below: …it is considered unlikely that septicity would be a problem.
Chapter 5. PROFCORP: Problem element
Increased noise levels are not expected to be a problem. …and operational noise is not considered to be a problem
When problem occurs with existential ‘there’, as in the two examples below, ‘be’ also seems to be acting as an event verb, but in this case as a result/effect verb as it has the meaning of ‘arise’. …there should not be any disposal problem. …it will be unlikely that there will be a problem…
This use of ‘be’ with problem is also evident in the Condition-Consequence relation, although there is only one occurrence of this causal relation: If there is a problem with … It is interesting to note that in addition to the two tokens of problem occurring with existential ‘there’ which co-occur with ‘be’ acting as a result/effect verb, only two other of the 29 tokens for problem collocate with verbs signalling result/ effect (e.g. generate, derive), rather than with cause/reason verbs, e.g.: The problem derives primarily from degradation of…
The tokens of problem in both the relations of Means-Purpose (6 tokens) and Means-Result (2 tokens) collocate with implicit causative verbs (e.g. improve, ameliorate, alleviate, mitigate, prevent, avert, solve) which, as shown above, signal the Solution element, thereby according them the status of two-way signposts (e.g…. by solving the problem of… for the Means-Result relation). One important observation concerning these two-way signals is that they tend to occur in the same lexico-grammatical environment as the Evoking keywords (e.g. noise, odour) for the Problem element (see Chapter 4, Table 4-2), e.g.:
To ameliorate the future traffic noise problem, a package of …
…all solid materials removed to prevent an odour problem.
These findings on explicit and implicit causative verbs are thus in keeping with other research on cause-effect markers where the use of causative verbs far outweighed ‘result’ verbs, both in terms of types and the number of tokens (Flowerdew 1998b).
Non-causal categories for problem There are only 2 tokens for problem which do not fall into one of the causal categories. These could be viewed as having the status of ‘evaluating the problem’,
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but in a positive sense, e.g. …no insurmountable problem to the water supply is envisaged. In the following section, I will analyse the causation and non-causation based tokens for problems in PROFCORP which are also itemised in Table 5-1 above. I will also compare problem and problems to see to what extent different forms of a lemma pattern in a similar fashion or differently in professional writing.
Causal categories for problems There are many similarities between the distribution of the tokens for problems and problem in PROFCORP across the five semantic causal categories. First, the majority of these tokens (18 out of 20) in the Reason-Result category combine with causative verbs. Only three of these [18] tokens collocate with verbs signalling result/effect (e.g. Pollution problems could occur…; Where potential problems may arise …), while the remainder are divided between explicit and implicit causative verbs. The explicit verbs for cause/reason occurring with problems are cause (4) result in (4) create, and (1) present (1). Modals and other mitigating expressions, similar to those occurring with problem, are used to signal that these problems, in the main, refer to possible ones arising from planned construction work, e.g.: …that could cause odour and potential health problems. …are not expected to result in significant odour problems.
One difference, however, lies in the choice of verbs with problem and problems. ‘Be’ in the sense of ‘create’ occurred with problem (e.g. … are not expected to be a problem), but did not occur with problems. These data suggest that certain causative verbs may prefer singular or plural nouns, or indeed have a tendency to collocate with premodified nouns, and that factors such as these have a bearing on verb + noun collocation which are not considered in collocational dictionaries. The string ‘be a problem/problems’ was searched in the Applied Science written domain of the BNC and it was found that there were 26 instances out of 30 where ‘be’ had the meaning of ‘create’ (e.g. … only a few faces are supplied and this may be a problem for anyone running wordprocessing…). In contrast, this construction was not found in the 15 instances of ‘be problems’, i.e. with the lemma in the plural, where ‘be’ was always found with existential ‘there’ having the meaning of ‘arise’ (e.g. there might well be problems with klystrons…). Although the tokens for problems in the Means-Purpose relation collocate with similar verbs to those for problem in PROFCORP, and also cover a large range of verbs (e.g. address, reduce, resolve, deal with, remedy, minimise, prevent,
Chapter 5. PROFCORP: Problem element
overcome, solve), the Theme/Rheme patterning is different. Whereas three out of the six tokens for problem occur in Theme position, all the tokens for problems occur in Rheme position. This is no doubt because of the long clauses postmodifying problems, or a following subordinate clause as in the examples below. … detailed design to remedy the noise problems identified prior to the construction of.. …will be required to overcome the anticipated traffic problems on Lung Mun Road and the junction.. …has been drawn up to address the potential main problems identified above, so that …
In fact, one key difference between the tokens for problem and problems is that problems is usually premodified across all causal categories (32 out of 35 cases) and also has various forms of postmodification in the Means-Purpose category outlined above. It was found that problem was premodified in 18 out of 39 cases, usually when it occurred in the Means-Purpose relation, and on the few occasions when it was postmodified, this postmodification was in a brief prepositional phrase, e.g. The problem of leachate ….
Non-causal categories for problems The remaining 16 tokens for problems which cannot be classified under any of the causation categories are in sentences where the main function is to denote the existence of the problem as in the examples below. And once again, we find a combination of problems with technical Evoking lexis, e.g. groundwater: The same problems exist with mobile cooling units. The factory workers have at times identified problems with groundwater …
In the following section I will analyse the lexico-grammatical patterning for the noun need, which like problem and problems, also explicitly signals a negative evaluation, although it was not found as a key word in PROFCORP. I will focus on the nominal form only so that the analysis is compatible with the analyses for problem and problems and can be carried out under the same analytical framework.
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Analysis of need Causal categories for need In PROFCORP there are 118 tokens for need of which 62 are verbal, 53 nominal, and another three (also nominal) acting as headings. Out of the 53 tokens where need is acting as a noun, 14 tokens (slightly over 25%) are causation-related. In these 14 tokens, explicit and implicit causative verbs play a prominent role in realising causation. For example, there are 4 explicit verbs in various grammatical patterns, e.g.: …will, in turn, generate a need and … This will give rise to the need for… …leading to the need to review …
The other six phrases all employ an implicit verb signalling some kind of partial resolution of a problem, e.g. …will minimise the need to divert clinical waste.. …will avoid the need to dredge and dispose of …
In two phrases, the pattern There is …a need to/for… is involved in a GroundsConclusion relation, summing up a previous stretch of text, which is signalled by therefore: There is a need, therefore, for a dedicated-purpose built facility. There is therefore a need to minimize landfill gas emissions.
In fact, there were seven other cases where need was found in this patterning, four of which were in the negative, signalling absence of a need (which is also encapsulated by the pattern without the need to/for…, occurring 3 times) e.g.: There is no need to employ a specialist contractor. There was no immediate need for gas extraction.
Now, these other examples of this patterning with need are also involved in a causation relationship, but are not explicitly signalled by a marker such as therefore. Although Crombie (1985) mentions that there are very few cases in which Grounds-Conclusion can be inferred, the above examples indicate that there may well be exceptions. And, in fact, an examination of the wider context of these seven sentences reveals that three of these do signal Grounds-Conclusion, wrap-
Chapter 5. PROFCORP: Problem element
ping up a previous sub-section of text by proposing a solution, e.g. There will be a need for irrigation water.
Non-causal categories for need The remaining tokens for need in PROFCORP (40% of the total) refer to identification or establishment of a particular need, where the attribution is usually to a specific organization or previous documentation. Another observation about these examples is that the verbs are usually in the present perfect aspect indicating that some kind of problem exists which sets up the conditions for a proposed solution. The EIA has identified the need for a flyover.
In sum, like problem and problem, need has also been found to be involved in causation-based relations, and it remains to be seen whether these items operate in a similar way in STUCORP (see Chapter 7).
Analysis of impacts and impact In this section I analyse the lexico-grammatical patterning of two Evoking items, impacts and impact, in PROFCORP and compare them with the patterning for the Inscribed signals of problems and problem. The other Evoking items will not be examined separately as many of these, both the non-technical (e.g. noise, traffic) and technical (e.g. Dba, leachate) lexis, have been found to collocate with impacts and impact (see Table 4-2 for a list of these Evoking items). If the other Evoking items were examined, there would be a lot of unnecessary repetition; hence, this analysis of impacts and impact also serves as a template for analysis of the other Evoking items in PROFCORP. No analyses of Evoking items in STUCORP will be carried out as the only key keyword Evoking item, stolen, occurs just 49 times across 6 different texts.
Causal categories for impacts I begin the analysis with impacts as this item has the highest frequency of keyword occurrence among all the Evoking items in PROFCORP, surfacing as a keyword in 50 out of the 60 reports. Out of a total of 991 tokens for impacts, five are verbs and 30 of these act as headings or sub-headings which have been discounted from the following analysis. It is interesting to note that when impacts occurs
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Table 5-2. In-text tokens for impacts and impact in PROFCORP Causal relation
No. of tokens IMPACTS
No. of tokens IMPACT
Reason-Result Means-Result Grounds-Conclusion Means-Purpose Condition-Consequence Total (causal relations) Others Overall total:
384 13 16 82 2 497 459 956
214 18 11 51 2 296 356 652
as some type of heading it is usually in combination with other Evoking items, e.g. ‘environmental’, ‘noise’. Here, as I have argued in Chapter 4, when this type of sub-technical vocabulary is in a multi-word unit it takes on a technical meaning. Of the remaining 956 tokens for the noun impacts, 497 (i.e. 48%) have a causative function. A breakdown of these according to the five semantic categories of causal relations is given in Table 5-2. In common with the Inscribed signals for causation, the Reason-Result category is by far the most prominent, with 384 tokens out of the 497 (i.e. 77%) occurring in this class. In the Reason-Result category, 12 tokens for complex prepositions were found with impacts, with ‘due to’ occurring nine times. Out of the 15 adverb tokens (‘as a result’, ‘therefore’, ‘hence’, ‘consequently’, and ‘thus’) occurring with impacts, only five were found to belong in the Reason-Result category, as ten of these signalled the Grounds-Conclusion relation. However, it was the explicit and implicit causative verbs which largely defined the ReasonResult category. The explicit causative verbs signalling cause/reason number 75 tokens. Of these cause and result in are by far the most common occurring 31 and 22 times, respectively with impacts. In this patterning, impacts had a tendency to be premodified by general classifiers (e.g. environmental, ecological) as in the example below: Option 1 will result in greater ecological impacts than Option 2.
Because potential environmental impacts are being referred to, the lexico-grammatical patterning for impacts with causation verbs contains a variety of mitigating expressions, which are very similar to those for problems and problem, e.g. …an access road could result in significant impacts. … are unlikely to cause any additional environmental impacts to the adjacent environs.
Chapter 5. PROFCORP: Problem element
Other explicit causative verbs occurring with impacts include: generate (5), create (5), lead to (3) pose (2), give rise to (2). What is noticeable, however, is that of the three transitive causative verbs cause, create and generate, it is only generate which is found in the passive form, and here, all five instances are in some type of passive construction, e.g. Several schools will be subject to road traffic noise impacts generated from Roads… As I have suggested, this is a type of lexical colligation (i.e. the grammatical company that a collocation keeps). In the context of these environmental reports impacts has a strong tendency to collocate with a variety of explicit causative verbs which have a colligational preference for the active voice (except for generate which favours the passive), as was also found to be the case with verb + noun collocations of problem and problems in PROFCORP. Because the collocational and colligational patterning of impacts is so similar to that of problems, this Evoking item seems to be functioning as a covert synonym of problems, most likely because it is a type of sub-technical vocabulary, a lexical item used in general English which also takes on a specialised meaning in certain fields. I argued in a previous section that when problem collocates with various parts of the verb ‘be’ it takes on the semantics of a causative verb as this idiomatic phrase ‘be a problem’ (e.g. it is considered unlikely that septicity would be a problem) has the meaning of ‘cause / create a problem’. Likewise the phrase ‘have…. impacts’ also implies the meaning of ‘cause’ or ‘create’. There are five instances of this use in PROFCORP, as exemplified by the phrases below: … works for these pipelines will have negligible impacts. … major site activities which are likely to have noise impacts.
‘Be a problem’ and ‘have … impacts’ thus fit Sinclair’s (1991) idiom principle, although the examples in PROFCORP demonstrate that there is more internal lexical variation in the case of ‘have … impacts’. These examples also provide evidence for the polysemy of ‘be’ and ‘have’ in certain lexical phrases, just like many other words of the language (see Moon 1998 for a discussion and examples of polysemy in fixed expressions and idioms). I will now examine those implicit causative verbs occurring with impacts, which act as two-way signals for both the Problem and Solution element. The verbs occurring with this function are ‘reduce’, ‘minimise’, ‘mitigate’, ‘prevent’, ‘avoid’, ‘decrease’ and ‘control’. There are 61 tokens of these verbs in total, with reduce and minimise by far the most common occurring 28 and 21 times, respectively, usually in the active voice which is very similar to the colligational patterning of the explicit causative verbs with impacts. However, we also have another type of patterning operating here, the combination of Inscribed signal + Evoking
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item. The two verbs reduce and minimise which explicitly signal the Solution element, were identified as keyword Inscribed signals in PROFCORP (see Table 4-1), and here are found to combine with the Evoking item impacts. This keyword patterning is also prevalent in the Means-Purpose and Means-Result relations, as we shall see later. It has already been noted that there are 75 tokens for impacts occurring with explicit causative verbs such as ‘cause’, ‘create’, ‘result in’ etc., signalling the cause/ reason relation. The number of tokens for impacts found with the other explicit verbs signalling the result/effect relation is 80. This is a striking difference compared to the distribution of such verbs with problem and problems where only two out of the 26 tokens for problem and three out of the 18 tokens for problems collocated with verbs signalling the result/effect rather than the cause/reason relation. An analysis of the explicit result/effect verbs occurring with impacts shows that these are limited to only three verbs: arise from (54), result from (19) and occur (7). There are also 17 cases where impacts occurs with existential ‘there’, plus a verb indicating the future. As I have pointed out in the previous section, in these cases ‘be’ is acting as an implicit result/effect verb as it has the meaning of ‘arise’: There will be no adverse visual impacts. There will be no significant noise impacts during the operational and maintenance period.
As for the explicit result/effect verbs, occur has a different colligational patterning from the other two, arise from and result from. Occur was always found as a finite verb (e.g….and no significant impacts will occur), whereas both arise from and result from also occur in reduced relative clauses. In this respect, occur is similar to ‘be’ and ‘have’, which are all found in sentences where a more general reference is made to the impacts. In contrast, arise from and result in are found in more complex clauses and sentences where more precise information is given, as explained below. In the case of arise from, 30 out of the 54 tokens are of the participial variety as part of a defining reduced relative clause. These were equally divided between the Theme and Rheme parts of the sentence, as in the following examples: The major impacts arising from the above activities would be seawater quality, noise and dust. The report addresses all potential environmental impacts arising from construction and operation of the proposed LRWT.
Chapter 5. PROFCORP: Problem element
The verb result from also displayed a very similar colligational and Theme/Rheme patterning. Out of the 19 occurrences, 10 were of the form resulting from, as shown below. Land use impacts resulting from the modified master plan configuration are similar to impacts assessed in the New Airport Master Plan EIA. This form of mitigation would significantly reduce the scale of impacts resulting from the AFRF project.
Although there are 80 explicit verb tokens of three different types (result from, arise from and occur) signalling the result/effect aspect, there are also 89 tokens of the preposition ‘from’ with impacts. Here, ‘from’ has a very similar function to these verbs, as it can be considered as a reduction of ‘arising from’ or ‘resulting from’: Potential impacts from road traffic noise have been assessed.
This causative function of ‘from’ is listed as an entry in COBUILD (p. 584, entry no. 24), but in the two COBUILD examples below from does not have the same grammatical status: The committee’s enquiry arose from representations made by Basildon district Council. It’s a spin-off from military and space research.
In the first example above, from is part of an intransitive phrasal verb, whereas in the second it is part of a prepositional phrase, a type of reduced relative clause with a causative function which is its use in the combination of impacts + ‘from’. Now, this causative use of ‘from’ raises a question. Usually, prepositions are regarded as structural or grammatical words, but in this case ‘from’ has a lexical rather than grammatical orientation as it operates more like a content word. This data therefore questions the polar divisions of words into open class sets (like nouns) or closed class sets (like prepositions). Another question to ask is when ‘from’ is preferred to ‘arising from’ or ‘resulting from’ – an issue which has been raised previously. An inspection of impacts with resulting from, arising from and from reveals that in this grammatical construction arising from or resulting from seem to be used when the sentence structure adheres to the simple pattern of subject + verb + complement, and the sentence itself is quite short, as in the example below: The major impacts arising from the above activities would be seawater quality, noise and dust.
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However from seems to be preferred when either the complement part of the verb is complicated involving a succession of post-modifying clauses, as in (a) below, or when from is found in a rankshifted phrase within the nominal group, as in (b). Normally, we think of contextual and situational features as affecting lexical choice within the sentence, but here we appear to have cases where the internal sentence grammar has a bearing on this aspect. (a) Noise impacts from construction activities have been predicted to be within the HKPSG criterion for all unrestricted periods except at the isolated housing at Peng Chau where the limit will be exceeded for a short period of time. (b) The potential sources of water quality impacts from the construction of the Plant will be similar to typical land based construction activities which involve construction run-off and ……
Another phrase which is not normally considered as signalling result/effect, but which appears to have this function in this context is associated with, of which there are 35 instances in the data. Two of these examples are given below: Noise impacts associated with traffic serving the barging point are minor and would only increase noise levels marginally. Water quality impacts associated with construction are therefore likely to be minimal.
Here, associated with seems to be somewhat ambiguously involved in a causal effect and could well be being used euphemistically in scientific writing as a hedging device, more in the sense of ‘correlated with’ rather than ‘caused by’, most probably in line with the discursive practices of this particular discourse community. In this way, scientists would avoid claiming a direct causal effect which would forestall any challenges from their peers, especially when controversial issues are involved (see Hyland 1998). Concordancing this phrase in the Applied Science domain of the BNC shows that it occurs 1327 times in 162 texts. An investigation of the concordance lines selected on a one per text basis reveals that in 40% of cases it clearly has a negative semantic prosody, but one that is probably attenuated: The commission is concerned about the possible risks associated with releasing genetically altered organisms….
In a paper on corpus semantics, Stubbs (2001a) argues that the conventionalised view that pragmatic meanings are usually inferred by the reader/listener may be overstated and that large-scale corpus studies can provide evidence to show us that pragmatic meanings can also be conventionally encoded in linguistic form.
Chapter 5. PROFCORP: Problem element
This may well be the case with ‘associated with’ which appears to have a weaker pragmatic force than other causative markers. See Flowerdew (2008d), who, based on corpus evidence, makes a case for associated with being classified as a ‘material’, i.e. happening verb, rather that a ‘relational’ one in this specialised genre. An analysis of the PROFCORP data of impacts in the Reason-Result category has shown that as in common with problems, the Evoking item impacts favours collocation with explicit and implicit verbs for both the cause/reason and result/effect functions. One striking difference is that impacts is accorded a much greater degree of specificity, as evidenced by its collocational and colligational patterning with arise from, result from and from. What is particularly noteworthy, though, is that complex prepositions (e.g. ‘due to’, ‘because of ’, ‘as a result of ’) for cause/reason, and adverbs (e.g. ‘therefore’, ‘hence’, ‘thus’) for result/effect have been shown to be much less common than one would have originally supposed. 82 tokens were recorded for the Means-Purpose category, covering 12 different verbs, with minimise (29), mitigate (18) and reduce (16) occurring the most frequently with impacts. By far the most common grammatical construction used to express the Means-Purpose relation was (in order) to + infinitive; there was only one example of each of the following constructions: ‘in such a way that’, ‘so that’, ‘so as to’ and ‘in order that’. Moreover, it is interesting to note that there were only ten cases where a verb + impacts occupied Theme position in the sentence, as in the example below: To mitigate these adverse impacts additional mitigation was incorporated into the design.
In eight out of these ten cases impacts was premodified by these. As noted in Chapter 3, when such nouns are premodified by determiners the anaphora is carried by the determiner (in this case these), and the noun assumes an evaluative function. In the above example, adverse therefore has an intensifying function. In contrast, in the majority of cases where a nominal group containing impacts occurs in Rheme position, impacts was found not to refer to any specific entity within the text, as in the examples below: (a) The objectives of this supplementary Environmental Impact Assessment (EIA) are summarised as follows: to define….; to identify …….; and to recommend measures to minimise any adverse impacts to within established guidelines and standards. (b) special procedures were recommended for the dredging and disposal of the contaminated mud to minimise potential impacts.
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(c) The landscape plans presented with visual assessment form the basis of a comprehensive landscaping and tree planting programme designed to ameliorate the visual impacts of the scheme.
In (a) above any adverse impacts is exophoric as the impacts referred to are recoverable from the situation rather than from the text. Any adverse impacts can be interpreted as ‘any potential impacts that could arise in the future’. This is similar to Halliday and Hasan’s (1976: 71) example of Don’t go, the train’s coming, which Halliday and Hasan suggest interpreting as ‘the train we’re both expecting’. Likewise, potential impacts in example (b) can also be paraphrased in a similar manner to the phrase in (a), while in (c) an examination of the wider context reveals that the visual impacts of the scheme refers not to specific impacts mentioned previously in the text, but rather to ‘any potential impacts occurring in the future’. An analysis of the Means-Purpose relation therefore suggests that when impacts occurs in a phrase without any specific anaphoric or cataphoric reference it has a strong tendency to occupy Rheme position in the sentence. When impacts occurs in the Means-Result relation (13 instances), it is found with the same two-way signalling verbs (e.g. minimise, mitigate, reduce) as were found in the Reason-Result and Means-Purpose relations. The grammar used to express the Means-Result relation is always the verb in the passive followed by ‘by’ or ‘through’ + noun/-ing, with impacts always in Theme position and acting as an A-Noun: These noise impacts can be mitigated by noise barriers. The impacts will be minimised by maximising the use of materials from the site excavation into the reclamation and site formation fill materials.
The Grounds-Conclusion relation is signalled by a variety of complex prepositions and adverbs. The five tokens for In view of… and the single token for In consideration of… are all sentence-initial, with impacts having anaphoric reference to some kind of environmental problem elaborated on in the previous text, as in the example below: In view of these potential impacts, the EIA concluded that every opportunity must be taken to minimise potential impacts on Sousa arising from the construction works.
However, the other signals of the Grounds-Conclusion relation, namely As a result (4), Therefore (2), Thus (2), Hence (1), and Consequently (1) are invariably used to indicate the lack of a major problem, where impacts occurs with an evaluative adjective:
Chapter 5. PROFCORP: Problem element
Consequently, no significant impacts would result upon the marine environment. As a result, no adverse environmental impacts are expected.
Non-causal categories for impacts The remaining 459 tokens for impacts, which do not fall into any of the causation relations examined above, mostly cover the evaluation of the Problem element. These specifically relate to the monitoring and assessment aspect of the impacts and are usually accompanied by a specific noun modifier (e.g. dust, noise) as exemplified below: Model (FDM) was used to examine potential dust impacts during construction. Noise impacts were also assessed from the proposed transport terminus.
It was noted that in the causal categories impacts had a very similar patterning to that of problems and for this reason can be seen as acting as a type of covert synonym for problems. However, in the non-causation categories, problems has a superordinate role, with impacts acting as a hyponym, which is reinforced by its premodification by adjectives such as noise and dust in the above examples. These findings highlight the value of the ACRONYM project (Renouf 1996) referred to earlier, which has as its objective the automatic retrieval of hyponymic elements through collocational profiling, i.e. by identifying the most significant collocates of the superordinate term. The following section will examine the various lexico-grammatical patternings of impact and compare these with impacts to determine to what extent, if any, different forms of a lemma pattern differently.
Causal categories for impact There are 745 tokens for impact, which occurs as a keyword in 26 out of the 60 reports. Table 5-2 in the previous section gives a breakdown of the tokens for impact across the five different causation categories. Out of the total of 745 tokens for impact 88 of these act as headings and sub-headings and 5 are of a verbal form so these have been excluded from the following analysis, leaving 652 tokens for both the causal and non-causal categories Although there is a difference between impacts and impact in terms of the total number of tokens and their keyness across reports, their distribution across
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the different causation categories is remarkably similar. For example, 45% of the total number of tokens for impact (excluding headings and verbal forms) has a causative function, which is very close to the percentage of the tokens for impacts, with 48% having this function. However, within the causal relations the percentage distribution is also very similar. 72% of the tokens for impact fall within the Reason-Result category compared with 77% of the tokens for impacts. Likewise, 17% of the tokens for impact and 16% of the tokens for impacts fall within the Means-Purpose category. Moreover, as the following analysis will demonstrate, there are many similarities between the lexico-grammatical patterning of impact and impacts within these five causal relations. The main difference between impact and impacts lies in the frequency distributions of the lexico-grammatical patternings, as explained below. First of all, it was the explicit and implicit causative verbs occurring with impact to signal the cause/reason relation which were most prominent in the Reason-Result category, as was also found to be the case with impacts. There was a total of 98 explicit causative verbs with the following number of tokens for each of the following types: cause (22), result in (16), generate (4), with 55 tokens recorded for the phrase have an impact on and one token for the more forceful phrase exert an impact. These tokens for explicit causative verbs with impact make up 46% of the total number of tokens in the Reason-Result category, whereas they only comprised 19% for this category with impacts. The main reason for this difference lies in the fact that the semi-formulaic phrase ‘have an / any impact / impacts’ is found 55 times with the singular noun, but only five times with the plural. This phrase was checked in the Applied Science component of the BNC where only 33 instances of impacts were found, but 661 examples of impact occurring across 187 texts. To simplify the checking procedure a download of one example of impact from each text was searched which revealed that some variation of the basic pattern ‘have an impact’ occurred in 50 out of the 187 lines, i.e. 27%. In contrast, only 3 instances of the pattern with the plural lemma were found out of the total of 33 tokens, i.e. (9 %). These results thus show that different forms of a lemma of a semi-formulaic phrase can indeed manifest quite different patterning in terms of frequency distribution. An examination of the tokens of impact in the result/effect relation of the Reason-Result category shows that exactly the same kind of patterning occurs, but, again, with quite different frequency distributions. For example, the explicit verbs with impact (e.g. ‘arise from’, ‘result from’) totalled 19 tokens (8%) whereas these totalled 80 (21%) with impacts in the result/effect relation. This difference can be accounted for by the fact that such verbs prefer the plural lemma in the lexico-grammatical patterning: The direct impacts resulting from the GIRPD works will be …. Furthermore, there were only 23 cases where impact was found with
Chapter 5. PROFCORP: Problem element
‘from’, in the sense of ‘arising from’, but 89 were recorded with impacts. These differences in patterning thus indicate that it may not be beneficial to lemmatise a corpus, as was discussed in Chapter 3, as it has been shown that different lemmas can have different behaviours. However, no striking differences were noted between impact and impacts with two-way signalling verbs such as ‘reduce’, ‘minimise’ and ‘mitigate’. In fact, the types and number of tokens (as a percentage of the tokens for the Reason-Result category) and the lexico-grammatical patterning were remarkably similar.
Non-causal categories for impact There are 356 tokens out of the 652 for impact which are not causation-related. Although the percentage of non-causation tokens for impact and impacts is almost identical (55% compared with 53%), like problem and problems, their functions are quite different. Whereas the tokens for impacts relate to the monitoring and assessment aspect of the impacts, the other tokens for impact are mostly found in the Introduction section of the reports, focussing on the scope and background of the studies. In many instances impact is part of the multi-word item Environmental Impact Assessment (and hence a technical term) prefacing the abbreviated form EIA (see Chapter 3 for a discussion on how abbreviated forms are treated in PROFCORP). It is therefore not surprising that most of the tokens for impact are found in the Introduction sections where abbreviated forms are usually given in full for their first mention, as in the example below: The Environmental Protection Department commissioned ERM Hong Kong to carry out an Environmental Impact Assessment (EIA) to assess the potential environmental impacts involved.
Conclusion This chapter has thrown up some interesting findings regarding the lexico-grammatical patterning of selected signals for the Problem element analysed within a causal framework. The analysis has shown that causality plays a much greater role in shaping the lexico-grammatical patterning of key words signalling the Problem element than one might have initially supposed. It has been shown to permeate the type of discourse under investigation and therefore supports Trimble’s (1985: 59) premise that ‘… so many processes and other activities are expressed by scientific and technical discourse that relates actual or hypothetical causes and results’. At a textual level causality has been shown to be a factor in anaphoric
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referencing and Theme/Rheme patterning. At a more delicate level of the sentence or clause, various types of explicit and implicit causative verbs have been shown to be of particular importance in realising causation, surprisingly, more so than connectives. Besides these causative verbs, the verbs ‘be’ and ‘have’ in certain semi-formulaic phrases and existential ‘there’ with a future time marker have also been shown to be markers of causation. Other lexis, such as ‘from’ and ‘associated with’, also not normally viewed as indicators of causation, have been found to signal causal relations. The following chapter describes a similar analysis carried out for selected Evoking and Inscribed signals for the Solution element in PROFCORP, but within a more functional rather than notional, i.e. conceptual, framework.
chapter 6
PROFCORP Phraseological analysis of signals for the Solution element
The analysis of Inscribed and Evoking signals for the Solution element, like those for the Problem element in PROFCORP, is also based on the sub-categories of phraseology, i.e. collocation, colligation etc. outlined in the previous chapter. In this chapter the lexico-grammatical patternings of items for the Problem element were analysed within a classificatory framework of causative notions. However, a slightly different superordinate classificatory framework is proposed for some of the analyses in this chapter due to the differences in the nature of the signals between the Problem and Solution elements. Whereas all the signals examined in the previous chapter were nominal, the signals for the Solution element cover other grammatical categories including verbal and adjectival use (see Table 4-1). Those keyword Inscribed and Evoking signals which are nominal, e.g. recommendations, solutions, solution, and implementation, will be analysed at the highest level according to two broad functional categories – ‘Proposing a Solution’ and ‘Evaluating a Solution’, which are described in more detail in the following section. This is not to say that the lexico-grammatical patternings in which these nominal signals occur are not involved in any causal relations. They are, but they are dealt with under a broader system of functional analysis. The other main categories of signals which have been selected for investigation are the adjectival and verbal ones, which cover the items recommended and proposed (which could potentially belong to either category). These will be examined under the same causal categories as those in the previous chapter for the reason that the two functional categories listed above are superfluous for the starting point of this analysis as the function of ‘Proposing a Solution’ is intrinsic to the lexico-grammatical patterning of all the verbal tokens, and ‘Evaluating a Solution’ intrinsic to the lexico-grammatical patterning of the adjectival tokens for these two signals. These points are explained in more detail in a subsequent section. In addition to examining the Solution elements at a more discourse-based level, both from a notional and functional perspective, this analysis will also consider similar points raised previously, i.e. whether different forms of a lemma pat-
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tern differently, whether the patterns are marked interpersonally, and whether particular phrases can be associated with a particular Theme / Rheme position.
Classificatory framework: Functional categories for nominal signals The two main functional categories into which nominal items for the Solution element fall are as follows. Typical phrases from PROFCORP for solution are provided as examples. – –
Proposing a solution Evaluating a solution – Positive evaluation – Negative evaluation
The proposed solution is to use… …gives a more cost effective solution… …is unlikely to provide a fully effective solution.
Moreover, the positive and negative evaluation functional categories are not seen as polarities, but rather as operating on a cline. For example, the negative evaluation above is hedged, i.e. is unlikely to provide… rendering it less negative. Although it may seem rather anomalous to employ a different classification framework at the macro-level of analysis of keyword nouns for the Problem and Solution elements, with a notional one used for the former and a functional one for the latter for the nominal signals, this can be justified on the following grounds. According to Fillmore (1968, cited in Wilkins’ 1976) the logical relations existing between nouns and verbs: … comprise a set of universal, presumably innate, concepts which identify certain types of judgement human beings are capable of making about the events that are going on around them, judgements about such matters as who did it, who it happened to and what got changed.
The key concept here is ‘judgement’ and if we take a look at the example phrases in which the word problem occurs in the five categories of causal semantic relations outlined in Chapter 5 we can see that they are all judgmental in nature as they pertain to what is or what might be in the future. The Inscribed and Evoking phraseological items for the Solution element, on the other hand, fall under Wilkins’ category of communicative function Suasion, specifically the following: 4.2.1 Inducement persuade, suggest, advise, recommend, advocate, exhort, beg, urge, incite, propose (p. 46)
In contrast to the ‘judgmental’ aspect of the cause-effect conceptual category, this functional category of Inducement is seen by Wilkins as ‘influential’, i.e. as af-
Chapter 6. PROFCORP: Solution element
fecting the behaviour of others. The examples noted above for the functions of proposing and evaluating solutions clearly belong to this category as the main discourse purpose of all the reports in PROFCORP and STUCORP is to persuade, i.e. influence the readers of a recommended course of action to solve an existing problem as in the case of the STUCORP reports or a potential environmental one in the case of the PROFCORP ones. I will confine the following analysis to three nominal signals (recommendations, solution and solutions) and two adjectival / verbal signals (recommended and proposed) for the Inscribed lexis for the following reasons. As my aim is to examine the same signals in each corpus this would not be possible for the other items in PROFCORP, listed in Table 4-1, which only occur in that corpus. Moreover, minimise and reduce have already been discussed in the previous chapter as they were found to act as two-way signals for the Problem element. As for the six Evoking keyword items shown in Table 4-2, implementation, has been chosen for analysis as it is the only one which also occurs in STUCORP (but not as a key word). It is also of a more general nature than the other Evoking items such as barriers and ordinance and for this reason merits investigation as it is more likely to throw up patterns which include other Inscribed and Evoking items showing how these combine in the creation of discourse.
Classificatory framework: Grammatical / causal categories for adjectival and verbal groups As mentioned above, recommended and proposed, differ from the other keyword signals in that they are adjectival or verbal in nature rather than nominal. It has also been noted in the introduction to this chapter that it would not be very meaningful to investigate this lexis under the functional categories laid out in the previous section as the tokens for these signals automatically fall into the category of ‘Proposing a Solution’, where the signal is verbal e.g. Further insulation of noise source is recommended, or ‘Evaluating a Solution’, where the signal has adjectival status, e.g. It is believed that the use of the recommended mitigation measures should reduce impacts…. The two different grammatical categories therefore have a default functional value. Where appropriate, the lexico-grammatical patterning of these signals will be examined according to the same causal categories laid out in the previous chapter. However as the same signal can be either adjectival or verbal it is first of all necessary to assign each of the tokens for recommended and proposed to one of these two categories. Moreover, within the verbal category we also have to distinguish whether the signal is involved in an impersonal passive, subject accompa-
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nied by passive, active or clausal construction, as a particular structure may well influence its notional orientation in the discourse. For this reason, I have decided to commence the analysis of this category of items from a grammatical base at the primary level of delicacy, then moving to a more notional analysis of items under investigation in this category.
Analysis of recommendations Table 6-1 below presents a summary of the number of tokens for the Inscribed signals in PROFCORP, which tells us that this Solution element of the ProblemSolution pattern is realised by mainly adjectival / verbal signals in PROFCORP (i.e. recommended, proposed). There are 107 tokens in total for recommendations, which occurs as a keyword in eight of the reports in PROFCORP (see Table 4-2). Twenty-two of the 107 tokens constitute main headings, of which 10 have a dual function. Eight are part of a heading labelled ‘Conclusions and Recommendations’ and two part of a heading ‘Summary and Recommendations’, which leaves 85 in-text tokens. Of these, 77 tokens can be considered as an aspect of ‘Proposing a Solution’, although by virtue of this lexis they are also inherently evaluative in nature. The analysis of the lexico-grammatical patternings of these 77 tokens for recommendations in the ‘Proposing a Solution’ category will consider the choice of verbs collocating with this noun and the grammatical environment in which these collocations appear, i.e. the lexical colligations. Firstly, 43 of the 77 tokens occur with various verbs in the active voice. This patterning has a cataphoric function of indicating the content of the report. Verbs such as ‘present’, ‘highlight’, ‘summarise’ and ‘put forward’ in the present simple tense are found in this context, as in the examples given below:
Table 6-1. In-text tokens for Inscribed signals in PROFCORP Inscribed signals
PROFCORP No. of tokens
Recommended Proposed Recommendations Solutions Solution
* 400 * 584 * 85 9 31
* Occurs as a key word in four or more reports.
Chapter 6. PROFCORP: Solution element
This executive summary highlights the findings and recommendations of … This report presents a summary of the main findings and recommendations…
However, when verbs such as those above occur in the past tense, they have an intertextual function as they make reference to a previous study. The EIA considered details and … provided recommendations for monitoring ….
In other instances of intertextuality, recommendations has more the sense of ‘requirements’ when it occurs with verbs such as ‘address’, ‘meet’ and fulfil’: The external design of the CIF should meet the recommendations of….
In this context, recommendations was found to occur in a Means-Purpose relation in a few instances: A detailed survey has been carried out to fulfil recommendations…
Secondly, recommendations also co-occurs with verbs in the passive voice in 26 out of the 77 tokens. The most common verbs occurring in this patterning are make (8); summarise (5) and provide (5): The main conclusions and recommendations are summarised as follows: Recommendations are provided for monitoring and audit requirements: Recommendations are made to cover the inclusion of necessary infrastructure.
A closer examination of the verbs make, summarise and provide shows that they apparently display different colligational patterning. Both provide and make are commonly followed by some type of Purpose statement, as evidenced by the above examples. Also, the delexical verb make with recommendations only occurs once in the active, but eight times as a finite passive, thus suggesting a preference for the passive over the active. However, this was not found to be the case with summarise and provide, which were used in both active and passive voice. Another observation is that the thematisation of recommendations seems to be related to its positioning in a particular section of the PROFCORP reports. Where recommendations occurs as the Theme and is followed by the passive voice in the Rheme part of the sentence (e.g. recommendations are summarised as follows…) it is found in the Conclusion section of the reports. On the other hand, where it occurs in Rheme position and is preceded by the verb in the active voice (e.g. This report summarises the main findings and recommendations…) it is found as part of the Introduction sections.
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Only eight of the tokens for recommendations occur in the category of ‘Evaluating a Solution’, i.e. in sentences which are overtly evaluative in nature, signalled by adjectives such as adequate and valid in the sentences below: Therefore, the recommendations made in Section A should be adequate to mitigate… Visual and landscape impacts and mitigation recommendations described in the PDS2 remain valid…
Interestingly, all these eight evaluative sentences display the same lexico-grammatical patterning, with recommendations forming part of a nominal group containing a rankshifted clause, e.g. …recommendations made in Section A...; …recommendations described in the PDS2…. In fact, made occurs seven times in a nominal group, e.g. …preliminary recommendations made in this report… Therefore, the PROFCORP data show that when the delexical verb ‘make’ collocates with recommendations, it prefers a passive form, either as a main clause verb proposing a solution or in a rankshifted reduced relative clause as part of an evaluation of a solution. However, a cross-comparison with the 246 tokens of recommendations in the Applied Science component of the BNC reveals that although ‘make’ is one of the most frequently occurring verbs with recommendations, found 36 times, its patterning is quite different with 26 phrases found in the active, and only 10 in the passive. The reason for this is that the BNC data contains more interpersonal markers as subject, e.g. We made our recommendations about 18 months ago…, or the report is personalised, e.g. The Copenhagen report … makes recommendations for…. There were also very few reduced relative clauses which have an intertextual function, with only a handful of examples noted, e.g. …recommendations made in an International Atomic Energy Agency study in 1990. This comparison thus highlights the greater intertextuality and impersonality of the PROFCORP reports, contextual factors which are of crucial importance in professional writing (Bhatia 2004).
Analysis of solutions and solution Of the 16 tokens for solutions in PROFCORP seven of these act as headings which, like recommendations, have a dual function e.g. ‘Environmental Constraints and Solutions’. Of the other nine in-text tokens (see Table 6-1), two occur in the Introduction section of the reports, indicating their objective, and can thus be classified as ‘Proposing a Solution’:
Chapter 6. PROFCORP: Solution element
The central focus was to develop solutions to maximise the development potential…
The remaining seven tokens for solutions occur in the body of the report and belong to the ‘Evaluating a Solution’ category as they are all preceded by an evaluative adjective such as ‘practical’, ‘preferred’, ‘appropriate’, ‘possible’: Currently housekeeping measures are the only practical solutions to minimise release of contaminants from ….
In contrast, the singular form, of which there are 32 tokens altogether, patterns quite differently. Firstly, there is only one heading and this refers to a specialised term. Various specialised terms are also found with 20 out of the 31 tokens for solution in the text of the reports, e.g. ammonia solution, dredge solution, engineering solution. The remaining 11 tokens belong to the ‘Evaluating a Solution’ category, but unlike the tokens for solutions which carry a positive evaluation, most of these have an aspect of negativity associated with them, e.g.: Any scheme of pumping leachate water from … is unlikely to provide a fully effective solution. The AFRF is an interim solution to be operated whilst a permanent supply option is pursued.
Therefore, solution patterns quite differently from solutions in PROFCORP. In order to verify whether these observations are generalisable, I consulted the Applied Science component of the BNC. Here, 597 tokens of solution were recorded in 168 texts, and 1215 tokens for solutions in 200 texts. An examination of the downloads (one per text) revealed that in the case of solution there were some similarities. 45 out of the 200 tokens for solution were premodified by a specialist term, which were more or less equally divided between chemical terms such as copper-sulphate solution, formaldehyde solution and computer-related terms such as shrink-wrapped software solution, parallel processing solution. Another 35 tokens of solution were found to occur in sentences which carried negative evaluation, e.g. … the client must learn that avoidance is never an appropriate solution for their anxiety, with another 20 tokens showing positive evaluation. Unlike the tokens for solutions in PROFCORP, which always carried positive evaluation, those for solutions in the BNC were both positively and negatively weighted, with 16 and 14 examples recorded for each, respectively. Both the PROFCORP and the BNC data thus show that there is a tendency for solution to be more negatively oriented than solutions. This is quite a surprising finding as I would not have expected solution to have this negative semantic prosody as its inscribed nature is positive.
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Table 6-2. In-text tokens for the adjectival and verbal categories of recommended and proposed in PROFCORP Grammatical category
Premodifying adjective Impersonal passive Subject + passive Active Other clause construction Total
Recommended No. of tokens total
% of
Proposed No. of tokens % of total
95 102 135 22 46 400
24% 25% 34% 5% 12% 100%
437 36 57 5 49 584
75% 6% 9.7% 1% 8.3% 100%
The following section deals with an analysis of the adjectival and verbal Inscribed signals in PROFCORP.
Analysis of recommended Table 6-2 presents the number of tokens for recommended and proposed in the adjectival and various grammatical categories that these two similar types are found in. The focus of the subsequent analysis is on the lexico-grammatical patternings of recommended and proposed within these adjectival and various verbal categories. The extent to which these types are involved in some type of causal relation, either explicitly or implicitly, will also be examined. Below, I provide example sentences extracted from PROFCORP to illustrate how recommended is used in the various grammatical categories presented in the above table. Grammatical categories
Example sentences
Premodifying adjective
Provided that the recommended mitigation measures are diligently implemented, it is considered that construction activities will cause only local and temporary disturbance. It is recommended that suitable colouring and planting schemes be used.
Impersonal passive Subject + passive Ambient dust monitoring is recommended at the residential developments. Active The EIA study has recommended that guidelines on good site construction practices are included as contractual controls. Mitigation measures recommended for the construction phrase will generOther clause construction ally apply to maintenance dredging.
Chapter 6. PROFCORP: Solution element
Out of the 422 tokens for recommended, 22, i.e. approximately 1 in 20 of the tokens, are either headings or sub-headings, which again shows the significance of Inscribed signals for textual patterning. A random download of 2000 tokens of recommended (the maximum number allowed) of the written component of the full BNC reveals that only 25 of these function as headings or sub-headings, a ratio of 1 to 80, which underscores the significant use of this type as a heading in these reports. Of the remaining 400 tokens approximately 25% are of an adjectival and 75% of a verbal form, as can be gleaned from Table 6-2 above. A detailed analysis of these adjectival and verbal categories is given below.
Recommended as premodifying adjective The most salient noun to collocate with recommended is ‘measures’ which occurs 36 times in some type of noun phrase, with the specific pattern ‘recommended mitigation measures’ occurring 25 times. An examination of these 36 noun phrases where recommended collocates with ‘measures’ reveals that of these 25 (but not the same 25 as mentioned previously) are found in a causal relation. The Condition-Consequence relation is represented the most frequently, 14 times, with seven tokens for ‘if ’ and seven for ‘provided that …’ as in the example below: Provided that the recommended mitigation measures are diligently implemented, it is considered that construction activities ….
There are six examples of implicit causative verbs, with one in a Purpose clause. Notably, all of these are the three occurring as keywords in PROFCORP, namely, minimise, reduce and ensure, which act as two-way signals, e.g.: It is believed that the use of the recommended mitigation measures should reduce impacts at nearby ASRs to acceptable levels ….
The remaining 59 tokens for recommended, when used as a premodifying adjective, collocate with a semantic set of nouns (e.g. levels, criteria, requirements, plan, limits), which like the phrase ‘recommended mitigation measures’ signify some kind of monitoring, e.g. ‘recommended control levels’, ‘recommended water quality levels’, ‘recommended monitoring and audit requirements’. Therefore, in this context the 95 in-text tokens for recommended are shown to have a very tight collocational patterning semantically.
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Recommended in impersonal passive 102 of the 400 tokens of recommended (i.e. 25%) occur in an impersonal passive construction, with 89 found in the present simple form: It / it is recommended that … In contrast, of 2000 randomly downloaded tokens for recommended from the written component of the BNC, only 99 were found in this form, i.e. 2%, which shows the significance of this pattern in PROFCORP. The high frequency of this pattern across reports from different companies is a reflection of the conventionalized style of writing for this specific type of EIA report. It was noted in Chapter 3 that companies often have their own “template” for writing such reports and this “template” could well specify not only report divisions and sub-divisions, but also signalling phrases such as It is recommended that … When this grammatical construction is used, an analysis of its meaning within the wider context of the data shows that it enters into some aspect of causality, either explicitly or implicitly. When it occurs in some kind of explicit causal marker, this marker is operating at a local level of coherence: In order to avoid this water reserve area, it is recommended that the high rise development be located to the west of the water works. Due to the high dust levels in the area, it is recommended that monitoring is undertaken.
However when this phrase occurs without any accompanying causal marker, an examination of the wider discourse context reveals that in the majority of cases it falls into the Grounds-Conclusion rather than the more local discourse-type of Reason-Result. This is because it usually occurs at the end of a sub-section making a recommendation based on content in the preceding paragraph. Of course, concordancing can only tell us what linguistic features are present in a corpus. But it may be possible to verify this discourse feature through making It case sensitive as sub-sections would tend to begin a new sentence or paragraph. For example, under a sub-section headed ‘LANDSCAPE AND VISUAL IMPACT ASSESSMENT’, the Problem is stated in the first part as follows: Both the construction and operation of the proposed steel mill has potential to result in some landscape and visual impact. … Only the following moderate visual impacts were identified: moderate visual impact on walkers in the Countryside Conservation Area…
This sub-section concludes as follows, without any explicit signalling of GroundsConclusion:
Chapter 6. PROFCORP: Solution element
It was also recommended that landscaping on the road boundary be used to extend the landscape framework and reduce the visual mass of the development.
Crombie (1985) notes that the Grounds-Conclusion relation is usually explicitly signalled, but that does not seem to hold true for this data in which there are only 18 cases where the Grounds-Conclusion relation is explicitly signalled via the following: therefore (14), thus (2), hence (1), on this basis (1): It is therefore recommended that funding and as much lead time as practicable should be made available prior to the commencement of construction …
In Downing and Locke (1992: 231) therefore, consequently and hence are classified as having ‘consequential’ meaning, and because of this, for this reason and so as having ‘causal’ meaning (see Table 6-3 below). However, as therefore in the above context displays a more causal meaning, as recommendations are made by the writer on the basis of previous evidence, I would prefer to consider it as belonging to the ‘causal’ category, and not the ‘consequential’ category, which denotes a fact-based causal connection contained within the propositional content rather than a speaker-based one. The above example of Problem and Solution elements from the same text support Fries’ (2001, 2007) research that the information that is placed in the Rhemes of the clauses of the Solution sections of the texts ‘are cohesively tied to the description of the problem and thus address meanings that have already been brought to attention and made salient in the text’ (Fries 2007: 1). In the above extracts the Problem element is expressed as result in some landscape and visual impact with the Solution occuring in the Rheme, matching the Problem through Table 6-3. Conjunctive Themes (from Downing & Locke 1992) Meaning
Example
Additive Adversative Alternative Appositive Causal Comparative Concessive Conditional Consequential Continuative Temporal
Also, in addition, besides However, on the other hand, yet, conversely Alternatively, either … or, instead That is, for instance Because of this, for this reason, so In the same way, likewise Nevertheless, anyway, still In that case, under the circumstances Therefore, consequently, hence In this respect, as far as that’s concerned First, then, next, presently
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the cohesive lexis of reduce visual mass. Fries (ibid.: 18) notes that ‘Focus on this relation [i.e. matching of Problem-Solution] is achieved when the cohesive tie is presented as New information in the Rheme’, although these ideas have already been mentioned previously in the description of the problem. Fries concludes that such notions as Given and New and Theme and Rheme need to be examined in relation to the rhetorical purposes of the text segments in which they occur. In the PROFCORP data the prevalence of the pattern It / it is recommended that ... suggests that it may well be being used as a device for setting up this kind of matching relation. Usually the Purpose aspect of the Means-Purpose relation is found in Theme position, but there are 12 cases of the pattern ‘it + verb + recommended that…’ with a Purpose clause in Rheme position. Moreover, these purpose clauses tend to be longer in length incorporating postmodification of a noun with a prepositional phrase, as in the following example: It is recommended that suitable colouring and planting schemes be used, in conjunction with screening walls, to minimise the visual/landscape impact of these buildings.
The foregoing analysis thus reveals that in this data ‘it + verb + recommended that …’ has a strong tendency to occur with various causal markers and the type of causal marker signals whether the phrase is operating inter-sententially or at a more global level. Where there is no explicit causal marker, the phrase tends to have a summative concluding function.
Recommended in subject + passive construction The tokens for recommended which occur with a passive verb and a subject total 135, which is 34% of the total number of tokens for this type. In common with the impersonal passive, the majority of the tokens for recommended, 90 out of 135, occur in a phrase with a verb in the present simple tense, with 21 and 22 tokens with verbs in the past simple and present perfect respectively, and only two tokens occurring with a modal verb (will, can). First, this construction shows a slight colligational preference for plural nouns; in 60% of cases (i.e. 82 of the 135 tokens) the subject is in the plural form. Significantly, however, around 70% of these are made up of either Inscribed signals for the Solution element, (e.g. mitigation measures, practices, procedures, proposals), or Inscribed signals for the Evaluation element (e.g. requirements, tests, audits), several of which occur as keywords in four or more texts (see Table 4-). Examples of these for both the Solution and Evaluation elements are given below.
Chapter 6. PROFCORP: Solution element
Appropriate mitigation measures are recommended for each phase. Special procedures were recommended for the dredging and disposal of …. Environmental audits are recommended to check the effectiveness of mitigatory measures and thereby …
The remaining 40% of the tokens (i.e. 53 out of 135) for recommended in this construction are with a noun in the singular. An examination of these singular nouns reveals that they mostly fall into two distinct categories. Firstly, one category consists of Inscribed signals of both the Solution and Evaluation elements, which occur as key words in four or more reports (refer to Tables 4-1 and 4-2): A multi-system gas control scheme is recommended to minimize sub-surface lateral gas migration beyond site boundaries… An emergency response plan (ERP) is recommended to provide a written procedure for dealing with emergency situations such as … A detailed ecological impact assessment is recommended as part of the afteruse contracts for each site. Effluent monitoring is recommended for the ammonia solution from the nitrous oxide plant …
The other category comprises nominalisations, which happen to be of the grammatical metaphor type, a major feature of the discourses of science (Halliday 1998). The utilisation of quietened equipment … is recommended to minimise… Thus, provision of indirect technical remedies … is recommended to ensure… Regulation of privately delivered construction waste is recommended. The successful implementation of environmental measures is recommended.
Now, the next question to ask is when recommended in this subject + passive construction might be used instead of recommended in impersonal passive discussed in the previous section. It appears that when certain verbs (e.g. carry out, make, undertake and consider) are used, there is a preference for the impersonal passive, indicating that their nominal metaphorical equivalents, while possible, are not commonly used. It is recommended that a comprehensive environmental audit is undertaken to confirm that the odour control systems are operating…. It is recommended that a reassessment be made ….
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By virtue of its grammatical nature, the nominalisation occurs as Theme and recommended occurs in the Rheme part of the sentence. It is interesting to note that some type of Purpose clause is usually contained within the Rheme along with this patterning. Here, we have an example of what Hunston and Francis (2000) term ‘clause collocation’, as the data shows that recommended in this grammatical structure has a strong collocation with Purpose clauses. The following mitigation measures are recommended in order that all construction works for the LAR will comply with … Other means for noise reduction are also recommended to control noise emission at source…
Purpose clauses are also found in the Theme, but only on 10 occasions, and notably these are very short without any postmodification of the accompanying noun, e.g.: To reduce congestion, regulation of construction waste is recommended. To alleviate frequent flooding, drainage channels were recommended in the north…
The prevalence of Purpose clauses with subject + recommended thus demonstrates how intertwined the Problem and Solution elements are when solutions are being put forward.
Recommended in active voice In contrast to the two types of passive construction which make up 59% of the tokens for recommended, the active is found in only 22 cases, i.e. 5% of the tokens, comprising two distinct categories. In the first category, recommended signifies an aspect of intertextuality when it is used in the past tense in the Introduction of the reports. Here, reference is made to a previous recommendation by a decisionmaking body, which in turn constitutes the basis of the present environmental report. As socio-linguists belonging to the school of New Rhetoric point out: ‘No text is single, as texts refer to one another, draw from one another, create the purpose for one another’ (Devitt 1991: 336). In April 1993 the Land Development Policy Committee recommended that detailed planning and design for the first stage of development should include the first eight container berths ….
Chapter 6. PROFCORP: Solution element
The second sense in which recommended is used is when it occurs in the present perfect tense in the Conclusion section of the report, summarising recommendations contained in the Body of the report. The EIA report has recommended monitoring and audit of noise throughout the construction.
One striking difference between these two uses of recommended in the active voice and the uses of this token in the two passive constructions analysed in the previous sub-section, is that there are no explicit or implicit causal relations in this lexico-grammatical patterning. This highlights the role that tense and voice can play in determining causality, as it has been shown that when recommended occurs in a passive construction the majority of the verbs are in the present simple tense, assisting in the signalling of a Grounds-Conclusion relation.
Recommended in other clause constructions In this type of construction, recommended occurs 46 times, accounting for 12% of the tokens for this type. Where the clause is a relative one, in only three cases does it occur as a full clause. In all other cases recommended stands for a defining reduced relative clause, always postmodifying, as in the following example: The Sousa mitigation measures and controls recommended in this Report for the construction stage should be incorporated in the detailed design of the AFRF.
Recommended occurs 12 times in the phrase ‘as recommended’, which is also a type of reduced clause. It is noticeable that in all cases ‘as recommended’ is never in sentence initial position, but always occurs after the proposition has been introduced, as in the following example: Contamination mud in the reclamation area will be dredged using a sealed grab as recommended in EPD Contaminated Spoil Management Study….
The phrase ‘as recommended’, I would argue is a signal of intertextuality with a similar function to the phrase ‘recommendations made in…’. Its frequency is salient, i.e. of rhetorical importance, in these reports as this phrase only occurs 52 times in the whole written domain of the BNC. An examination of these lines shows that it overwhelmingly collocates with ‘by’, but there are seven instances where ‘as recommended’ is followed by ‘in’, usually referring to some type of report. The following section will analyse the tokens for proposed and compare its functions in the different grammatical categories with those for recommended to note the similarities and differences between these two tokens.
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Analysis of proposed In the Collins Bank of English Thesaurus (1998) ‘recommend’ and ‘propose’ are listed as synonyms of each other, but my data show that there is little similarity in the distribution of ‘recommended’ and ‘proposed’ across the broad adjectival and verbal categories. While the proportion of adjectival to various kinds of verbal types is 25% to 75% for recommended, this is reversed for proposed. The adjectival and various verbal categories in which proposed occurs, i.e. impersonal passive, subject + passive, active, and clauses, are examined below and compared with those for recommended.
Proposed as premodifying adjective Proposed as a premodifying adjective collocates with a quite different set of nouns from those collocating with recommended. Two general patterns for proposed are noted. In 37 cases proposed collocates with the superordinate noun ‘Development / developments’, which is also found to be the most frequently occurring noun with proposed. In the other cases, proposed collocates with nouns which denote a specific type of construction or development being proposed in these EIA reports. These more specific nouns occurring with proposed cover a wide range e.g. flyovers, container terminals, highways, landfill extension and marine parks, to name just a few. It would seem that in the context of these environmental reports it is the different semantic sets of nouns found to collocate with recommended and proposed, i.e. their different semantic preferences, which determine the meaning of these two seemingly similar adjectival forms. According to this data, we can say that recommended is used with a more restricted set of nouns, to refer to some kind of guidelines for monitoring purposes, whereas proposed has more the meaning of a suggestion. But a trawl through the Applied Science component of the BNC revealed that this distinction was not found between recommended and proposed in this subcorpus (both forms were used for referring to some kind of monitoring methods or procedures.) This distinction therefore seems to be specific to the PROFCORP data and alerts one to the danger of overgeneralising, and applying the findings from small-scale specialised corpora to a wider domain of the same general topic area (see Gavioli 2002).
Chapter 6. PROFCORP: Solution element
Proposed in impersonal passive 36 tokens, i.e 6% of the tokens for proposed, occur in the impersonal passive. In common with recommended, the majority of tokens, 33 out of 36, are found in the present simple tense, with two in the present perfect and one in the past simple tense. Proposed is also found to have a very similar causal patterning to that of recommended in this construction. Where it combines with a causal marker in the Theme part of the sentence, this is operating at a local level of coherence, as in the following: Due to the increasing demand for land and berthing facilities…, it was proposed as part of the CTB study that the reclamation should be extended. To ensure timely completion of the works, it is proposed to carry out an environmental assessment of the extension project in house.
However when proposed occurs without any initial causal marker (22 out of 36 tokens), like recommended in the impersonal passive it has a more global function as it indicates a Grounds-Conclusion relation. The following sentence concludes a sub-section headed WASTE ARISING. Again, there is no explicit textual theme such as ‘therefore’. It is proposed to leave the marine sediments of Tamar Basin in-situ… in order to leave these contaminated sediments undisturbed.
Proposed in passive + subject construction The tokens for proposed which occur with a passive verb and a subject total 57, which is almost 10% of the total number of tokens for this type. The number of tokens found in the various tenses is as follows: present simple (27), present perfect (20), past simple (8), and one token occurring with ‘can’ and one with ‘may’. An examination of these tokens in context reveals that the majority of them in this aspect fall in the last 20% of the report, which is the concluding section where the verb is found in the present perfect tense (cf. Gledhill 1995) e.g.: Detailed radiological monitoring has been proposed, including monitoring in the vicinity of the …
However, like recommended, 60% of the tokens (34 out of 57) occur with a plural noun as subject in this construction. Although 10 of these 34 subjects comprise an Inscribed signal for the Solution element, with eight examples of measures and two of recommendations noted, the remaining instances focus on Evoking items which do not occur as keywords. Examples of both types are provided below.
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Fifteen measures were proposed to avoid or mitigate air quality impact…. Several new access roads are proposed for the site.
When proposed occurs with a subject in the singular, these nouns are very similar to those found with recommended, i.e. they are either Inscribed signals for the Solution and Evaluation elements, or they are nominalisations, as in the examples below: An audit system is also proposed for both construction and operation phases… … the adoption of solar energy has therefore been proposed.
Proposed in active voice The five tokens for proposed which occur in the active voice are all found in the Introduction section of the reports. Like those tokens for recommended in the Introduction, they have an intertextual function as they refer to a previous document which forms the basis of the present investigation: A Project Steering Group (PSG) was convened by Government in 1991 to assist in planning this flyover and proposed three possible alignments which are referred to herein as Options A, B and C.
Proposed in other clause constructions Interestingly, clauses are the only verbal category in which proposed is used with the same degree of frequency as recommended. In all aspects, it has the same semantic and syntactic characteristics as recommended. Except in four cases out of 49 instances, proposed constitutes a reduced relative clause. It also collocates with similar nouns, which are mostly Inscribed signals for the Problem element, e.g. ‘measures’, ‘scheme’ and ‘construction’. Moreover, ‘as proposed’, in common with ‘as recommended’, is never found in sentence-initial position. As for Inscribed items for the Solution element in PROFCORP, the above analysis suggests that the Solution element tends to be realised through the adjectival and verbal signals, recommended and proposed, rather than through the nominal signals recommendations, solutions and solution. Moreover, the data indicate that recommendations and solutions may have a preference for different lexico-grammatical patternings, with recommendations having an important intertextual function and solutions a largely evaluative one. The analysis has also suggested that recommended and proposed are not synonymous and are not used
Chapter 6. PROFCORP: Solution element
interchangeably: recommended seems to be preferred in the passive construction whereas proposed usually occurs as a premodifying adjective.
Analysis of implementation There are six evoking items in PROFCORP, which occur as keywords in four or more reports. Out of these implementation has been chosen for analysis because it is the only one of the Evoking items, which also occurs in STUCORP (44 tokens) and also because it is the Evoking item which has the most general meaning, as mentioned earlier. 133 tokens are recorded for implementation, but only three of these are used as sub-headings, thus indicating the more superordinate nature of the Inscribed signals, which are used as sub-headings more than the Evoking items such as implementation. This also suggests the importance of the Inscribed lexis as sub-headings for creating textual coherence. The 130 in-text tokens for implementation are equally divided between the categories of ‘Proposing a Solution’ and ‘Evaluating a Solution’, with 65 tokens occurring in each, which are analysed below. Of the 65 tokens for implementation under ‘Proposing a Solution’, approximately a third of these (21 tokens) have the status of a noun modifier with the most common noun collocation being ‘implementation programme’, which occurs nine times. Moreover, the majority of the tokens (43 tokens) for implementation are found in the Introduction sections of the reports, either referring to the objectives or the different phases / stages of the plan to be implemented, e.g.: The overall objective of the New Airport Master Plan was defined as a comprehensive scheme for the planning and implementation of an operationally safe and efficient airport.
Ten of the tokens of implementation concern a recommendation and are found in the Concluding sections, e.g.: It is recommended that a modified version of past institutional arrangements be adopted for the implementation of the LAPH developments.
There are also 65 tokens for implementation which can be classified as ‘Evaluating a Solution’ and here much greater conformity is noted as these tokens are involved in two distinct kinds of lexico-grammatical patterning which both entail an aspect of causativity. In the first pattern, of which there are 44 instances, implementation is preceded by a complex preposition (e.g. ‘as a result of ’) or a single preposition of the pattern: preposition + ‘the implementation of…’, signalling the
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Reason-Result relation. Such prepositions in this patterning are as follows: with (23); after (6); through (4); prior to (2); associated with (2); on / upon (2); as a result of (2); from (1); by (1); following (1). Although some of these prepositions are clearly causation-related such as ‘as a result of ’, ‘through’ and ‘by’, the others are not normally considered as relating to causation. I have already made a case in the previous chapter for treating ‘associated with’ as a hedged causative device. In this context, I would also like to argue that other prepositions such as ‘after’, ‘upon’ and ‘with’, like ‘as a result of ’, also signal Reason-Result as they indicate the outcome of a course of action, as shown in the following examples: With the implementation of the preferred mitigation option, the estimated number of dwellings exposed to traffic noise levels was reduced to 120. Residual noise impacts after implementation of mitigation measures will be within established standards and guidelines.
The co-occurrence of various prepositions or prepositional phrases with grammatical metaphor nouns such as implementation suggests that this type of patterning may be fairly typical of formal technical writing. And in fact a search in the Applied Science component of the BNC of the string ‘With the…’, which was the preposition occurring the most frequently in this sense, did throw up instances of such patterning, e.g. With the evolution of multicellular organisms…; with the completion of …; with the construction of…. One significant finding was that in the PROFCORP data the pattern – preposition / prepositional phrase + grammatical metaphor noun – always signalled a positive outcome of the proposed recommendation, thus suggesting that this patterning could have a positive semantic prosody. But where explicit causative verbs, e.g. ‘lead to’ and ‘result in’ were used, there was negative evaluation of a rejected solution. In addition, implementation of Option 1 would lead to significant disruption of traffic.
The other lexico-grammatical patterning of implementation is where it is thematised and followed by two-way signalling verbs such as ‘reduce’, ‘minimise’ and ‘ensure’, which are keyword Inscribed signals for the Solution element (see Table 4-1). There are 20 instances of such patterning with examples given below. The implementation of the mitigation measures will ensure that the project is carried out…. The implementation of Kam Tin Bypass will reduce traffic noise levels at residences along Kam Tin Road.
Chapter 6. PROFCORP: Solution element
This patterning for the Evoking item implementation is quite different from the patterning for the Inscribed signals in this category of ‘Evaluating a Solution’. Whereas the evaluation of a solution is realised by evaluative adjectives with the Inscribed signals, here it is realised by Reason-Result markers, either prepositions or causative verbs, to signal the successful / unsuccessful outcome of an implemented course of action.
Conclusion The analysis of the various signals for the Solution element has shown us that in PROFCORP as far as the nominal signals are concerned the tokens for recommendations are mostly concentrated in the category of ‘Proposing a Solution’, whereas the majority of the tokens for solutions are evaluative in nature. We have also seen that with respect to the ‘Evaluating a Solution’ category, solution is more negatively-oriented that solutions, e.g. … is unlikely to provide a fully effective solution. Turning to the verbal signals, there is far more uniformity in the lexico-grammatical patterning of recommended and proposed in the verbal categories than was found with the seemingly synonymous nominal signals. In the various verbal categories it is the subject + passive construction which is most illuminating as it shows the important role of nominalisations as grammatical metaphor nouns, e.g. …the adoption of solar energy has therefore been proposed. One keyword Evoking item, implementation, which also happens to be a grammatical metaphor noun, was found to have negative or semantic prosodies depending on the causative verb employed. A socio-contextual feature of writing uncovered by this analysis is the signalling of intertextual features by the various lexico-grammatical patternings of the key words. For example, …recommended in…, recommendations made in… and A project steering group proposed… were different ways in which this was accomplished. Using the classificatory frameworks outlined at the beginning of this chapter, the same Inscribed and Evoking items for the Solution element occurring in STUCORP will be analysed in Chapter 8. The following chapter presents an analysis of those Inscribed and Evoking items described in Chapter 5 for the Problem element in PROFCORP with reference to the STUCORP data.
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STUCORP Phraseological analysis of signals for the Problem element
In this chapter I analyse the lexico-grammatical patterning of key words for the Problem element in STUCORP using the same causal vs. non-causal categories as those used for classifying the PROFCORP data. My main aim is to see whether there are any similarities or differences between the patterning of these signals. With respect to the last point, I take it as axiomatic that STUCORP is, in the first place, primarily good data, which is a somewhat different perspective to previous research on learner corpora. In the last few years, much useful and valuable research has been carried out learner corpora most notably by Granger (ed.) (1998b) and Granger et al. (eds) (2002) with the establishment of the International Corpus of Learner English, ICLE (see Pravec 2002 for a comprehensive survey of learner corpora). Significantly, most of this research has focused on various types of error analysis in NNS student writing compared with NS writing of argumentative academic essays (see Granger 1998a; Milton 2000; Nesselhauf 2004a; Barlow (2005) provides an in-depth review of the types of errors in learner corpora and explanations of interlanguage features in learner writing). In this book, this distinction between NNS and NS does not apply as it is not a question of whether the writer is a native-speaker or not, although this obviously can have a bearing on writing proficiency, but rather whether the writer is an expert or apprentice writer. For this reason, I treat the Learner corpus as a corpus in its own right and examine the major findings from the perspective of whether the students appear to have mastered the language in accord with various contextual parameters. Comparisons are made between the Learner and the Professional corpus but these are not only for the sake of establishing students’ errors (although, of course various types of sentence-level deficiencies are important considerations), but are more for the purpose of ascertaining to what extent student writing is like or unlike expert writing, taking into account the different contextual and situational features of each corpus. It is not assumed that differences necessarily indicate deficiencies in student writing; nevertheless, the STCORP data cannot be regarded as having the same uncomplicated status as the PROFCORP data. Where differences do arise which
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may diverge from normal practice in English, I have consulted the Applied Science component of the BNC to establish whether this is a specific feature of apprentice writing or could be considered as competent writing of a different kind to that found in the expert corpus. In the following section the Inscribed signals problem, problems and need, which occur as key words and also as key-key words in STUCORP (see Table 41), are chosen for detailed analysis. The focus of this analysis is a comparison of these items with their counterparts in PROFCORP (see Chapter 5) to examine to what extent student writing mirrors professional writing. As no Evoking signals surfaced for the Problem element in STUCORP, the analysis does not deal with this category.
Analysis of problem and problems Table 7-1 below presents a summary of the in-text tokens for problem and problems in both STUCORP and PROFCORP based on the classificatory framework outlined in Chapter 5. It has already been noted that there were no examples of problem or problems acting as (sub)-headings in PROFCORP. However, there were 19 tokens of problem (4% of total) and 28 tokens of problems (10% of total) used as sub-headings in STUCORP. One reason for this is probably that students are assimilating into their own work the sub-headings used in several exemplar reports they have been exposed to in class teaching to familiarise them with the structure and content of typical recommendation reports. In fact, the materials contain a few exercises on the use of headings and sub-headings in which students are asked to assign either Table 7-1. In-text tokens for problem and problems in PROFCORP and STUCORP Corpus Inscribed signal (SUB)-HEADING CAUSAL RELATION Reason-Result Means-Result Grounds-Conclusion Means-Purpose Condition-Consequence Total (causal) Non-causal Overall Total (In-text)
Problem
PROFCORP Problems
Problem
STUCORP Problems
0
0
19
28
29 2 1 6 1 39 2 41
20 0 3 10 2 35 16 51
84 6 5 48 7 150 323 473
65 4 0 21 1 91 170 261
Chapter 7. STUCORP: Problem element
structural headings (e.g. ‘Problems’, ‘Possible Solutions’) or topical ones (e.g. ‘A new computerized system’). It is therefore not surprising to find problem / problems as sub-headings in a couple of these reports.
Causal categories for problem The most striking observation about the in-text tokens for problem in STUCORP is that only 31% of them (150 out of 473) can be categorised according to the five semantic categories of causation, as presented in Table 7-1. In contrast, as we have seen in Chapter 5, 39 out of the 41 tokens of problem in PROFCORP are causation-related. I will first discuss the different kinds of lexico-grammatical patterning of problem in the Reason-Result category. Out of the 84 tokens of problem in STUCORP in this category, 20 were found to occur in the same grammatical environment as the following nouns: cause (10), causes (7), reason (1) factor (1) and, factors (1). Surprisingly there was only one instance of its occurrence with a complex preposition, i.e., Such problem may be due to the fact that …. There were 43 tokens for problem which collocated with various types of explicit verbs marking causation, thus indicating negative semantic prosody. First of all, the following explicit causative verbs (11) were used: cause (4), lead to (2) with one token for each of the following verbs: bring, create, become, pose, and incur. As with the data for this type of verb in PROFCORP, they were mainly used in the active voice with only one example of a passive (These kind of problem are caused by …) and one example of a reduced relative clause (The general financial problem caused by …). In the analysis of these verbs in PROFCORP a case was made for treating ‘be’ as a causative verb in a few cases, but this function of ‘be’ was not found in STUCORP where one problem leading to another, a progressive multilayering of the problem (one of the variations of the pattern described in Chapter 1) was expressed by verbal substitutions in the phrase: This … problem (e.g. This causes a problem…; This may create a problem.). Nine examples where students had tried to use causative verbs denoting result/effect were recorded. Problem was used with come from (e.g. …and the other problem came from …) in two examples and with rise / arise in seven. However, this verb was only used correctly in two cases, which were of the pattern … problem … has arisen. The main reason for the incorrect use of this verb in STUCORP is that students are confusing an explicit causative verb marking result/effect with ones for cause/reason, as exemplified below:
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* (a) It rises a problem that … * (b) The problem seems to be arised out of the fact that …
In (a) a cause/reason verb such as create should be used. In (b) the passive voice, which signals cause/reason is used, whereas the active voice should be used here with problem to signal result/effect. Now I will examine the 23 tokens for problem collocating with implicit causative verbs which can either have a positive or negative semantic prosody. Whereas in PROFCORP all such verbs had the meaning of ‘make the problem better’, in STUCORP these can be divided into two groups: 18 phrases where the verb (e.g. solve) has a positive semantic prosody, and five phrases where the verb has a negative semantic prosody to convey the meaning that the problem is exacerbated in some way. This difference between the use of implicit causative verbs in the two corpora can be accounted for by the Situation in which the Problem is positioned. In PROFCORP the environmental problems discussed in the reports are mainly potential ones which could arise from any planned construction work, whereas in STUCORP the problems already exist, as evidenced by primary and secondary source data in the student reports. With regard to those five implicit verbs with a negative semantic prosody occurring with problem, they were either not used correctly or only marginally so. In some cases the student had attempted to use an implicit verb as an explicit one, as in the example below: * This situation will deteriorate the problem of …
In other cases, the student had used the passive, e.g. …the problem will probably be worsened, but in native-like English (Pawley & Syder 1983) this concept would more likely be expressed by an explicit causative verb + noun, derived from the implicit verb, e.g. …will lead to a worsening of the problem. In fact, a cross-comparison with the BNC shows that out of 272 instances of ‘worsened’, only 14 of these were in the passive, and always past tense, thus strongly suggesting that the passive use, although possible, is not usual. Another reformulation for this ergative verb could be …the problem will worsen. As pointed out by Celce-Murcia (2002) such types of ergative verbs are particularly problematic for ESL writers, noting that overpassivation is a common type of error made by advanced learners who have yet to master the middle voice (ergative) in their writing. As for the 18 implicit verbs denoting some kind of solution to the problem, there are 11 tokens for solve, two tokens for attend to, and one for each of the fol. I used the whole BNC here as the Applied Science component only contained 19 instances of ‘worsened’, only four of which formed part of a passive construction.
Chapter 7. STUCORP: Problem element 101
lowing verbs: resolve, ease, fix, reduce, get rid of. In the sentence below, not only is get rid of an inappropriate register for the formal context of recommendation report writing, but it is also incorrect semantically. The problem refers to the students booking the sports facilities and not turning up, so a more appropriate verb semantically in this case would be ‘resolve’ rather than ‘eliminate’, the more formal equivalent of ‘get rid of ’: In order to get rid of this problem, we had proposed two penalty scheme.
Furthermore, a check with the Applied Science domain of the BNC reveals that out of the 82 instances of ‘get* rid of ’, problem only occurs once and here it is postmodified (e.g. this gets rid of the problem of shrinkage and swelling). In all other cases, ‘get* rid of ’ is found with Evoking items (e.g. …shallow injection is best suited to getting rid of dirty water...), thus suggesting that Inscribed and Evoking lexis each have their own preferences for verb collocations, as was also noted for the verbs pose*, present* in Chapter 5. There was also one example where the student had substituted a preposition for a verb, e.g. …have a very good policy against the problem. In the whole BNC slight variations on the phrase, against … problem, are found 10 times, but always in the context of ‘encountering a problem’, e.g. …come up against a problem, and never in the sense of ‘solving a problem’. With regard to the two-way signalling of both the Problem and Solution elements, what we find with the STUCORP data, which we did not find in PROFCORP, is that this relation is very often expressed in a lexico-grammatical phrase containing the two nouns, ‘solution(s)’ and ‘problem’. In PROFCORP, this dual signalling was always expressed by a verb, e.g. minimise, reduce + the noun problem. However, out of the 20 tokens of problem in the STUCORP data, ten occur in a phrase where a specific solution is proposed. Of these, eight tokens of problem are found in the Rheme position of the sentence: … will be a possible solution to the problem. … may be a solution to this problem. …is not a technically feasible solution to the problem.
One reason for the occurrence of this kind of metalanguage for the Problem-Solution pattern in the STUCORP data could well be that students are over-relying on such metalanguage because they lack knowledge of the range of implicit verbs (e.g. alleviate, eliminate) found in the PROFCORP data. Another observation is that in the PROFCORP data modals such as would, should and could are used to convey the possible degree of success of the proposed solution, e.g. Daily, or more
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frequent covering of deposited waste with inert material should minimise much of the problem. However, in STUCORP in this context, there are no instances of these modals, with students using possible on only four occasions to convey this epistemic use, e.g. …will be a possible solution to the problem. This suggests that students either see no need for modal marking, or more likely, have a very limited repertoire of modal expressions, an observation which has also been made in a number of other studies on learners’ lack of epistemic devices to mitigate their claims (see Flowerdew 2000; Hyland & Milton 1997; Lorenz 1998). In the STUCORP data, the other ten phrases containing ‘solution(s)’ + ‘problem’ operate at a metadiscourse level, with ‘solution(s)’ acting cataphorically and ‘problem’ anaphorically beyond the sentence boundary, as in the examples below: … and to suggest some solutions to this problem. …we suggest some feasible solutions for the problem.
This kind of explicit signalling was not present in the PROFCORP data which may well be because two key sub-headings in the reports (‘Environmental Impacts’ and ‘Mitigating Measures’) fulfilled the same function, and therefore explicit signalling in the body of the reports was considered redundant. However, variations on this lexico-grammatical patterning ‘solution(s)….problem’ were found in the BNC, but this pattern was more common with ‘solution… problem’ (323 instances) compared with just 45 instances of ‘solutions … problem’. A similar type of explicit metadiscourse signalling was also found in the lexico-grammatical phrases for the 48 tokens of problem in the Means-Purpose relation (see Ädel 2006 for a corpus-based analysis of learner metadiscourse). In this category, there are 28 phrases which are a variation of the pattern ‘solution’ + (in order) ‘to solve’ + ‘problem’. Several examples are provided below: …we will suggest possible solutions to tackle this problem. …recommendation to solve the problem. …another method to solve the problem.
Of the remaining 20 tokens of problem in purpose clauses, one occurs in the grammatical construction ‘so as…’ (…so as to solve the present problem), one token is found after ‘so’ (…so the problem of … can be solved), and 18 are found in ‘in order to’ clauses, 11 of which occur in Theme position in the sentence, and 9 in Rheme position, a pattern and distribution very similar to those in the PROFCORP data.
Chapter 7. STUCORP: Problem element 103
It has already been noted that students have difficulty in using causative verbs and collocational appropriacy is also another area which poses some difficulty. Two verbs used by students, cope with (4) and get rid of (1) are a little informal for the context and it might have been better to have substituted ‘deal with’ and ‘resolve’ respectively. There are four tokens for ‘improve’ (e.g. …recommendations to improve the above problem). In these cases, it might have been more appropriate to have substituted ‘problem’ with ‘situation’ as what the students are referring to is an existing situation which is problematic, i.e. the lack of payphones on campus: The following are some recommendations to improve the above problem: to install more payphones campus…
A check with the Applied Science component of the BNC did not yield any instances of the collocation improve + problem, so the whole BNC was searched. This search revealed that ‘improve’ does occur with ‘problem’, but this is only in three cases and in all of them it is some kind of health problem which is being referred to, e.g. His surgeon has said that two years’ rest may improve the problem significantly. The lexico-grammatical phrases for both the Means-Result and ConditionConsequence relations tended to be rather formulaic, mostly of the pattern This problem can be solved by … for the former, and If there is a problem … for the latter. The phrases in the Grounds-Conclusion category (e.g. So, this problem is still in a controversial stage) were all evaluative in nature, which confirms the findings of previous small-scale research of student writing where causation was the rhetorical function under investigation (Flowerdew 1998b).
Non-causal categories for problem I now consider the role of the remaining 323 tokens for problem which cannot be classified under any of the five semantic relations denoting causation. 21 of these tokens were found in sentences relating to the aim of the investigation, e.g.: In this project our aim is to investigate seriousness of copyright problem in the Hong Kong University of Science and Technology.
Sentences such as the one above stating the objective of the project were not found in PROFCORP, where a statement reflecting the objective of the report was encased in a Means-Purpose relation with problem taking a plural form: An initial environmental impact assessment was commissioned with a view to identifying any insurmountable environmental problems…
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However, the remaining tokens for problem in STUCORP were found in the section of the reports on describing and discussing the findings, usually with the verb ‘be’. In the context of these student reports when problem is used with part of the verb ‘be’, this verb functions as a stative verb denoting the existence, or relating to the evaluation, of a problem, as in the examples below: A third problem is insufficient ink. …belongings unattendance is a very serious problem.
In 78 cases problem was premodified by evaluative adjectives such as common, important, significant, severe, serious, main, major. When premodified by certain of these adjectives (e.g. common, serious, severe, significant, important), it tended to be anaphoric, but a type of anaphora operating at the sentence rather than at the discourse level, e.g. … the lack of modem lines is really a serious problem. In several cases, students had used the referent ‘It’, e.g. It is really a serious problem, when ‘This’ might have been expected for retrospective reference (see Lin 2002 for a corpus-based study on the overuse and misuse of ‘It’ in the writing of Chinese learners of English). One striking use of premodification was that of the ordinatives such as first, second, third, and next, and the deictic another, with 12 tokens recorded for the ordinatives and 16 tokens recorded for another. Phrases containing ordinals (e.g. The next / second / third problem …) and those with main, major and minor, which usually combined with ordinals (e.g. The first major problem …) had cataphoric reference, always sentence-internal, with the main lexico-grammatical pattern being ‘problem’ + ‘be’ + noun, and a few cases of the pattern ‘problem’ + ‘be’ + ‘that clause’, as in the examples below: The second major problem is the power failure problem. The main problem is that the Division of Humanities could not allocate resources to establish such a centre now.
12 out of the 16 tokens for another premodifying problem also displayed a similar type of patterning, e.g. Another problem is / was (that) the…. These findings are consonant with Schmidt’s (2000) corpus-based research on the types of nouns classified as Inscribed signals in this article, who notes that ‘The lexico-grammatical use of ‘Problem’ nouns is marked by a distinct preference for the patterns N-be-that, th-N and th-be-N.’ (p. 122). In sum, these data therefore indicate that when students use problem with premodifying adjectives, the type of premodifying adjective accompanying it determines its anaphoric or cataphoric status. Another point to note is that in the
Chapter 7. STUCORP: Problem element 105
above examples, the anaphoric and cataphoric referencing is always, except in a couple of cases, sentence-internal. However, the most significant fact about the anaphoric and cataphoric referencing patterns associated with problem in STUCORP is that it is markedly different from that found in the previous sub-section, which examined the lexico-grammatical patterning of problem when it was involved in a causal relation. There, regardless of the causal category, problem was invariably anaphoric, but most importantly, operating beyond the sentence boundary (e.g. …recommendation to solve the problem). Likewise, in PROFCORP, the same kind of anaphoric referencing at the discourse level was present in the causation-related phrases (e.g. …should minimise much of the problem). [An exception to this was when the indefinite article was used to refer to a potential rather than an existing problem: e.g. …the effluent export scheme will create a noise problem]. Interestingly, Scott (2001b) found that problem had a more local scope than discourse-organising function in a corpus of Guardian newspaper feature articles, which seems to be the case when it is not involved in a causation-based relation. The functions of problem either operating as a local discourse signal or as a more global connective one, i.e. as an A-Noun binding adjacent clauses or sentences, or as an evaluative or as an evaluative one supporting the anaphoric status of the determiner This in the phrase This problem has already been brought up in Chapter 1 in the review of Vocabulary 3 items. Possible explanations for the differences in these two roles of problem are discussed in more detail below. According to Francis (1986, 1994), problem is one of the most common discourse-organising anaphoric nouns, which she terms ‘A-Nouns’ (Schmidt refers to such discourse organising nouns as ‘shell nouns’). Now, if we examine the example provided in Francis’ 1994 article (p. 85), we find that it is premodified by ‘this’, and also happens to be involved in a causal relation, signalled by ‘to get around’: …the patients’ immune system recognised the mouse antibodies and rejected them. This meant they did not remain in the system long enough to be fully effective. The second generation antibody now under development is an attempt to get around this problem by ‘humanising’ the mouse antibodies, using a technique developed by … (Francis 1994: 85)
Francis’ assumption is generally shown to be valid where problem in both STUCORP and PROFCORP is involved in some kind of causal relation. However, other data in STUCORP (i.e. that relating to non-causation phrases) does not support Francis’ premise. Moreover, Hoey (1998) also remarks that Francis seems to be overstating the anaphoric importance of these signalling nouns such as
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problem. He points out that nominal groups containing another also label a previous stretch of text, in this case as a problem, since another problem is given and requires the reader to relate both an earlier and later lexicalisation of problem to fully interpret it, thus suggesting that it functions both cataphorically and anaphorically. Also, as has been noted in Chapter 1, the anaphoric function of such nouns as problem is called into question when it co-occurs with a demonstrative such as This, which carries the anaphora rather than the noun problem. Consulting the Applied Science component of the BNC helps to shed light on this issue. An examination of the 222 concordance lines of This / this problem supports Francis’ notion of problem acting as an anaphoric noun at the discourse level. But this anaphoric use can be explained by the fact that 138 (i.e. 62%) of these instances are causation-based, with 131 combining with a verb signalling the Solution element, e.g. This problem was overcome by providing the lamps with locks. However, a totally different picture emerges if we examine the concordance lines for problem with other premodifiers in the same component of the BNC. For example, of the 36 instances of another problem, only five of these are anaphoric, where the anaphoric reference is always sentence-internal. The majority have cataphoric reference and are of the same patterning as those found in STUCORP. Moreover, only five of these are causation-based, all employing the verb ‘arise’. Not surprisingly, ordinatives also displayed similar patterning to that of another. As for evaluative adjectives (e.g. serious, common) examples from the same component of the BNC mentioned previously, show the lexico-grammatical patterning to be very similar to that found in STUCORP, i.e. having anaphoric reference within the sentence, e.g. … stray radiations become a serious problem…. However, in 8 out of the 20 concordance lines of serious problem, the anaphoric sentence referent is this, e.g. This is a serious problem…, which depends on a previous stretch of discourse for its relexicalisation. What is most significant, though, is that out of the 21 examples of serious problem and 20 examples of common problem examined in the BNC, there is only one instance of a causation-related sentence. Therefore, unlike the data for problem in causation-related phrases, these data do not support Francis’ premise that problem is a common A-Noun. We can therefore conclude from an analysis of the above data in PROFCORP and STUCORP and further examples from the BNC that Francis is correct in saying that problem functions anaphorically at the discourse level, but that this statement is mainly applicable to its role in causal relations. Moreover, the BNC data have also confirmed that problem when immediately premodified by this or the plays an important role in causal relations, which was not found to be the case with other premodifiers, such as evaluative adjectives and ordinatives. Another important aspect to note is that problem loses its status as an A-Noun when pre-
Chapter 7. STUCORP: Problem element 107
modified by This as the anaphor is carried by the determiner. As Schmidt (2000: 8) points out ‘shell nouns and shell-noun phrases can only be studied appropriately if what they link up with is taken into account’. However, these data from PROFCORP and STUCORP and comparative data drawn from the BNC highlight the importance of also taking into account the semantic relations that a noun such as problem may be involved in when determining its discourse-organising role (see Flowerdew 2003 for a discussion of these points). The tokens for problems are analysed according to the same causal and noncausal categories as those above.
Causal categories for problems 35% of the tokens for problems (91 out of 261) are causation-related, which is a similar proportion to those tokens for problem in PROFCORP. Interestingly, in the Reason-Result category explicit causative verbs for cause/reason were rarely used, the only example being ‘cause’ occurring three times with problem. Likewise, there were relatively few occurrences of causation nouns (7) and complex prepositions (2). However, there were 11 occurrences of some kind of explicit causative verb for result/effect, but in only one instance was the verb used correctly. In some cases there were syntactic errors in formation of relative clauses, which has been identified as a feature of Hong Kong English (see Gisborne 2000). * There were some problems resulted from low attendance… * There are some problems and mainly come from …
In other cases, though, it was the students’ lack of vocabulary which was found to be wanting. For example, in the sentence below dealing with appears to mean arising from. * … the problems dealing with computer barns…
There were four sentences where ‘happen’ or ‘appear’ were used in a rather unidiomatic way, as in the two examples below. These verbs would be better replaced by ‘occur’ or ‘arise’, which were found to collocate with problems in PROFCORP. * Generalised problems mostly happen in computer barns. * More and more problems appear.
As for the two-way signalling lexico-grammatical phrases conveying the resolution of a problem (which as I have argued is also a type of causation) these are also very similar to those for problem in STUCORP, which fall into two categories:
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implicit verb + problems, or noun + problems. The implicit verbs collocating with problems were very similar to those used with problem, with ‘solve’ occurring with 17 of the 25 tokens for problems. In the 15 cases where problems occurred with ‘solution(s)’ or a synonym, it always had anaphoric reference as did problem in this kind of construction, e.g. …possible solutions to these problems. As the patternings for problems in the Means-Purpose and Means-Result categories are almost identical to those for problem in these two categories, they will not be dwelt on further here.
Non-causal categories for problems I will now examine the remaining 170 tokens of problems, which do not belong to any of the causation categories. In common with those non causation-related tokens for problem, these either relate to the purpose of the investigation which is covered in the Introduction / Background section of the reports, or the reporting of the findings contained in the Body. However, one salient difference between the non-causation uses of problems and problem in STUCORP is that the distribution between the tokens for problems in these two broad areas is quite different. Whereas only 21 out of the 323 tokens for problem (i.e. 6.5%) relate to the purpose of the investigation, 77 out of the 170 tokens for problems (i.e. 45%) do so. A look at the lexico-grammatical phrases for these tokens reveals that statements indicating the purpose of an investigation favour problems over problem e.g. We would like to know what are the problems on computer usage they are facing. In a few cases, problems had a retrospective function as it occurred in the Conclusion restating the purpose, e.g. In this report we have analysed some existing problems in…. The use of problems in the report Introductions sets up the following discourse in the Body and no doubt explains why 10% of the total number of tokens for problems (28 out of 289) were used as a heading / sub heading in the Body, whereas only 4% of the tokens for problem had this function. The remaining 93 tokens for problems in STUCORP which were found in statements reporting the findings also displayed differences from those for problem with this function. First of all, nearly 65% of the tokens for problem had this function, whereas only 35% of the tokens for problems did. The main reasons for this are twofold and can be gleaned from the pre-modification patterns of problem and problems. The tokens for problems often occur in a kind of topic sentence, with pre-modification by adjectives such as several, some and main, e.g.: The result shows that there are several problems in …. We have discovered that the five main problems of services…
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However, in the analysis of the lexico-grammatical patterning for problem it was noted that these tokens were premodified by enumerator adjectives, e.g. The next problem…; A third problem…, which is an elaboration of the topic sentence, suggesting that one topic sentence with problems could generate two or three following phrases with problem. Although this type of patterning did not occur in PROFCORP it cannot be regarded as a feature of student writing only, as the same kind of patterning occurs with problems and problem in the Applied Science component of the BNC, as mentioned previously. However, it could well be that students are overusing these topic sentences incorporating sequence markers. In this respect, Hinkel (2002) found that sequence markers such as first, second etc. were substantially overused by Asian non-native speakers. To conclude, it can be seen that the distribution of problem and problems in STUCORP across the five causal categories is fairly similar to that of problem and problems in PROFCORP, with most of the tokens concentrated in the ReasonResult, and secondly the Means-Purpose relation. However, this analysis has revealed differences in lexico-grammatical patterning between different forms of the same lemma in both PROFCORP and STUCORP, thus lending weight to Hoey’s (1997, 2005) argument that different forms of a lemma pattern differently. For example, it was noted in PROFCORP that problems and problem had different premodification behaviour with problems premodified across all causal categories. Differences have also been noted in the lexico-grammatical patterning of the same lemma across the two corpora, such as the absence of interpersonal markers in the form of modal verbs with the tokens for problem in STUCORP. In PROFCORP such verbs were used as a mitigating device for making recommendations, but they were never found in STUCORP where they would have been appropriate in certain contexts, thus confirming the findings of several other corpus-based studies which note that student writing tends to be too direct and unhedged. Another difference in the patterning of problem was where it was used with a two-way signalling verb (e.g. ‘minimise’, ‘alleviate’) in PROFCORP. The verb ‘solve’ was overwhelmingly used by students, thus suggesting their limited vocabulary range. However, as already mentioned, differences between the lexico-grammatical patterning in STUCORP and PROFCORP do not necessarily indicate deficiencies, and this was found to be the case with topic-like sentences, e.g. We have discovered that the five main problems of services…, and sub-topic sentences, e.g. The second major problem is the power failure problem, which did not occur in PROFCORP, but were found in the Applied Science Component of the BNC. It is these types of sentences which account for the difference is the distribution of problem and problems between the causal and non-causal categories in PROFCORP and STUCORP.
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In the following section, I will analyse the lexico-grammatical patterning for the noun need, which like problem and problems also explicitly signals a negative evaluation of a situation and was found as a key-key word in STUCORP (see Table 4-1). I will focus on the nominal form only so that the analysis is compatible with the analyses for problem and problems and can be carried out under the same analytical framework.
Analysis of need Causal categories for need In STUCORP, out of a total of 354 tokens for need, 228 are verbal, 120 are of a nominal form and three act as headings. 28 tokens (23%) out of the 120 noun forms for need were causation-related, and in some respects similar to those in PROFCORP, but with more emphasis on the Solution element. Verbs such as ‘fulfil’, ‘meet’ and ‘satisfy’ were found to occur with need in 13 phrases, nine of which belonged to the Means-Purpose category, as shown in the examples below. The opening hours of CCST computer barns should be extended in order to meet the need of students. Its opening hours, 2pm to 2am, can satisfy the need of students.
However, the above differ from the use of need + ‘to’ or ‘for’ in PROFCORP as the combination of need + ‘of ’ in STUCORP is closer to the verbal use in that it relates to “students’ need”, i.e. what students need. As previously pointed out, such phrases act as two-way signals, with the verb signalling the Solution element and need the Problem element. This dual signalling necessarily entails causativity, although there is no explicit marker as such. Interestingly, 10 of the causation sentences in which need occurs contains an adverbial marker: Therefore (6), Thus (2), So (1), Hence (1), but only one of these is incorporated within the sentence, albeit with faulty sentence structure e.g. …influences the waiting time, thus usually there is no need to wait for a seat. All the other adverbs, however, are sentence initial, as in the examples below: Thus, there is no need to buy a new server. Therefore, there is a need to provide more payphones.
Usually, these adverbials are regarded in EFL textbooks as markers of local coherence, connecting two sentences, but in these examples, they are functioning at a more global level, as a summary conclusion for a previous stretch of text. For this
Chapter 7. STUCORP: Problem element
reason they can be classified as Grounds-Conclusion rather than Reason-Result; in this respect the students show writing maturity. However, in the phrases signalling Grounds-Conclusion in PROFCORP, the adverbial was always intersentential and never sentence-initial as in the STUCORP examples, so from this perspective the student writing can be considered as lacking variation in the positioning of adverbs.
Non-causal categories for need Another 20 tokens for need are of the pattern There is/was …a need to/for, with 10 tokens colligating with to + verb, e.g. There is a need to learn Putonghua. This type of colligation can be viewed as what Benson et al. (1986) refer to as grammatical collocation, i.e. the fact that the grammatical structure following need is ‘to’ + verb, ‘for’ + noun, or ‘of ’ + noun in the case of the student reports. However, we also have another type of colligation operating with need, which is the one defined by Hoey as ‘the grammatical company a word keeps’. This refers not to what the grammatical structure is, but rather to the preferred grammatical patterning, as it were (see Chapter 1 for a discussion of colligation). For example, in the case of need, one common patterning is with existential ‘there’. Also, need prefers the pattern There is no need to…, which is manifest in both the PROFCORP and STUCORP data. There are no examples of the alternative negative form There isn’t any need to… in either PROFCORP or STUCORP. Likewise, none were found for the near-synonym problem. To substantiate this point, I checked these two patternings (There isn’t any… and There is no…) with need and problem in the Applied Science component of the BNC. Any only occurred with need in three cases always with an intervening adverb, e.g. There is seldom any need for…; There is no longer any need for… and never with a verb in the negative. The preferred colligational patterning with problem was always There is no…, e.g. There is no problem with comparators. Data from STUCORP therefore show that students are aware of both the collocational and colligational patterning of need. Nevertheless, one grammatical infelicity shown below which is specific to the writing of Chinese learners of English is the confusion of anticipatory ‘It’ with existential ‘There’; Lin (2002) shows that this misunderstanding can be attributed to the influence of the students’ mother tongue. * It is no need to explain.
Another key interlanguage feature, that of topicalisation (e.g. For the problem, it can be solved by…, has been the subject of numerous studies (Green 1996) and
111
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also examined from a corpus-based perspective (cf. Green et al. 2000; Milton 2000). However, surprisingly, I did not find any evidence of this L1 transfer in my corpus data when examining the lexico-grammatical patterning of problem(s) and solution(s). One reason for this may be that we have addressed this point on a recurrent basis in our teaching materials so it is heartening to think that students might have improved in this area. The remaining tokens for need either centre around the purpose for the investigation in the Introduction or the identification of some type of need in the Body of the reports, which is very similar to the functions of the lexico-grammatical phrases for problems in STUCORP and those for need in PROFCORP. However, the orientation of the problem statements is quite different in the two corpora. In PROFCORP the problem is taken as ‘given’, i.e. already established, and it has been pointed out in the previous section that that the preferred patterning for this is, for example, The EIA has identified the need for a flyover. On the other hand, in the student reports the Problem statement is not taken as ‘given’ as part of the writing task is to provide evidence for the existence of some kind of problem. As pointed out in Chapter 3, student topics revolve around university concerns such as a shortage of computers or the lack of modem lines for dialling in. This lack, or shortage, was very often conveyed by the word insufficient which surfaced as a key word in five texts (see Table 4-1) and occurred 114 times in STUCORP, as in the examples below: Students think their laser-print quota is insufficient. …number of available computers in barns becomes really insufficient.
Having established the existence of a problem, the students then proceeded to comment on this in relation to students’ needs, which is why patterning along the lines of ‘cannot’ + meet/fulfil/satisfy + need of students was common. …the number of machines cannot meet the need of students. …this still cannot satisfy with the need of the students.
Although some lexico-grammatical patternings for need are similar in both corpora (e.g. There is a need …for/to), in other instances the patterning is quite different depending on whether the problem statement is being presented as already established or new. These examples thus show the importance of contextual parameters in determining and interpreting collocational features, which was emphasised in Chapter 3.
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Conclusion As for the STUCORP data, deficiencies have been found in certain respects. Student writing displays a very limited range of explicit and implicit verbs used for marking the cause/reason relation, and interpersonal markers did not occur with these verbs when they could have been used in some cases, thus supporting other corpus-based studies which conclude that student writing is too direct. Moreover, it was found that in the result/effect relation, there were many grammatical infelicities with students confusing these verbs with those for cause/reason. Students were also unaware of alternative semi-formulaic phrases, e.g. ‘this … be a problem’, with students also attempting to “create” their own lexical phrases to express an idea, which although grammatically correct sounded non-native like. However, some aspects of student writing were found to resemble professional writing. The lexico-grammatical patterning of sentences such as The second major problem is …, while not appearing in PROFCORP, were found in the BNC Applied Science component. The type of analysis which is the focus of this chapter thus demonstrates the value of corpus-based work for identifying both the causation and non-causation based lexico-grammatical patterning of keyword signals for the Problem element. However, as emphasised in the second chapter it is also necessary to have recourse to the wider context to explain why the professional or students writers construct the discourse as they do and this aspect has also been referred to in the analysis of need. The following chapter describes a similar analysis carried out for Evoking and Inscribed signals for the Solution element in STUCORP, but within a more functional rather than notional, i.e. conceptual, framework.
chapter 8
STUCORP Phraseological analysis of signals for the Solution element
The analysis of Inscribed and Evoking signals in STUCORP for the Solution element is based on the functional classificatory framework described in Chapter 6. Five Inscribed signals, recommendations, solution, solutions, recommended and proposed, will be analysed, in addition to the Evoking item implementation. As in the previous chapter, the focus will be on the similarities and differences in the patterning of these signals with their counterparts in PROFCORP.
Analysis of recommendations Table 8-1 below tells us that the Solution element of the Problem-Solution pattern is realized by mainly nominal signals in STUCORP (i.e. recommendations, solutions, solution), but by adjectival / verbal signals in PROFCORP (i.e. recommended, proposed). What is striking is that recommendations is the only signal which occurs as a key-key word in both STUCORP and PROFCORP. Table 4-1 has shown us that recommendations occurs as a keyword in five reports in STUCORP and also as a keyword in eight of the reports in PROFCORP. Of the 39 tokens for recommendations (excluding 11 headings and sub-headings) in STUCORP, 37 fall into the category of ‘Proposing a Solution’ and only two into Table 8-1. In-text tokens for Inscribed signals in PROFCORP and STUCORP Inscribed signals
PROFCORP No. of tokens
STUCORP No. of tokens
Recommended Proposed Recommendations Solutions Solution
*400 *584 *85 9 31
57 76 *39 *89 *118
* Occurs as a key word in four or more reports.
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the category of ‘Evaluating a Solution’. This is similar to PROFCORP where the majority of the tokens for recommendations also belong to the category of ‘Proposing a Solution’. It is not surprising that in STUCORP only two tokens fall into the ‘Evaluating a Solution’ category given the nature of these reports, as these are recommendation-based reports suggesting proposals which have not as yet been implemented and therefore not yet undergone any evaluation. The evaluative adjectives used are thus based on the writers’ own analysis of the situation rather than any hard and fast evidence, e.g.: ...we are able to propose some well grounded recommendations for improving the current …curriculum.
However, the lexico-grammatical patterning of recommendations in STUCORP is quite different from that found in PROFCORP. In the previous section it was noted in PROFCORP that the active and passive forms of the verbs collocating with recommendations displayed a preference for certain sections of the reports. For example, verbs such as ‘present’ and ‘put forward’ were commonly found in the present simple active in the Introductions, e.g. This report presents a summary of the main findings and recommendations, whereas verbs such as ‘make’, ‘summarise’ and ‘provide’ in the present simple passive form tended to be used in the Body of the reports, e.g. Recommendations are made…. However, no such patterning was present for the tokens in STUCORP. 23 out of the 37 tokens for recommendations are used in the Introduction sections of the reports, of which 15 are found with verbs in the active (e.g. ‘make’, ‘give’) and 8 with verbs in the passive. With the frequent use of the active voice, it is not surprising to find the interpersonal pronoun ‘we’ used, with the typical lexico-grammatical patterning for recommendations shown in those phrases below: In this report, we will provide recommendations to increase residents’ awareness on… We make recommendations for better project management.
This aspect of interpersonality was distinctly lacking in the PROFCORP data, where the patterning for recommendations in the Introduction sections was always of the type: This report presents … The remaining 14 tokens for recommendations occur in the Body of the reports and are used cataphorically. Interestingly, for some reason or other, students avoid using here the interpersonal pronoun ‘we’ which was prevalent in the Introductions. Instead we find the passive used, e.g. Thus the following recommendations are given,
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Or constructions such as: The following are some recommendations to improve the above problem There are some recommendations for their improvement.
Although the above are grammatically correct, an inspection of the 246 tokens of recommendations in the Applied Science component of the BNC reveals that this construction does not occur, thus suggesting that it is more typical of student rather than professional writing.
Analysis of solutions and solution Both solutions and solution occur as keywords in four of the reports in STUCORP, thus providing further evidence that students are heavily assimilating key vocabulary from the rubrics for this writing assignment into the writing up of their own recommendation reports (see Appendix 3-2). 106 entries are recorded for solutions of which 89 occur in the text of the reports as opposed to being used as (sub)-headings. Of these 89 in-text tokens for solutions, 58 can be classified under ‘Proposing a Solution’ and 31 under the category of ‘Evaluating a Solution’, which are both analysed in detail below. Exactly half of the 58 tokens for solutions in the category of “Proposing a Solution’ occur in the Introduction sections of the reports. In this section ‘we’ + a verb in the active is used in 35 cases with solutions, as in the examples below, but it is not used exclusively as was found to be the case with recommendations. …we will finger out the actual problem and suggest solutions to CCST for improving … …we will suggest some solutions and find out whether they are feasible.
Another major type of lexico-grammatical patterning in the Introductions is of the type: The goal / aim / purpose of this project / research / investigation is… e.g. The goal of this research is to find out …and to suggest some solutions to this problem.
There are also a few examples of a verb in the passive with variations in the verbs used, e.g. Some proposed solutions to the problems are then outlined and…
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Although the students are using a wider range of lexico-grammatical patternings here than those found with recommendations, their use of lexis is quite restricted. For example, it is found that students either use ‘suggest’ or ‘recommend’ with solutions. Moreover, it is also obvious from all the above examples that the assignment rubrics are being incorporated into the discourse through the constant repetition of problem / problems, which is found to collocate overwhelmingly with the verb ‘solve’ in phrases occurring in the Introduction sections of the reports in STUCORP. A different lexico-grammatical patterning exists for those 21 phrases with solutions found in the Body of the reports, although the juxtaposition of problem / problems with solutions in the same sentence is still prevalent, as seen from the examples below. The most common type of pattern is one where solutions has cataphoric reference and is used in a kind of topic sentence as in the examples below: There are several solutions recommended to solve the problem. Owing to the importance of the problem mentioned in this report, five solutions are suggested here…
The eight phrases with solutions occurring in the Concluding section act as summary conclusions. Of the 31 sentences with solutions which carry some kind of evaluation three contain a phrase acting as a two-way signal pointing to the Solution, such as the one below: The above solutions can reduce the average waiting time for a student to find an unoccupied computer….
The remaining 27 phrases include an evaluative adjective, with the most common being possible (13) and feasible (10). Moreover, feasible, in addition to ‘problem / problems’ and ‘situation’, seems to be another lexical item from the rubrics which has been incorporated by students into their writing. However these phrases in which solutions collocates with an evaluative adjective do not have quite the same status as those analysed in PROFCORP where an evaluation was only given after a specific solution had been proposed in the Body of the reports. In the STUCORP data, on the other hand, evaluative adjectives are integrated into the text in all the lexico-grammatical phrases analysed under ‘Proposing a Solution’. For example, in the Introduction sections possible is commonly used, whereas feasible tends to occur in the topic sentence introducing the Body of the reports, e.g.:
Chapter 8. STUCORP: Solution element 119
This report will analyse the problems in the system and suggest some possible solutions to resolve it. As a result, we suggest some feasible solutions for the problem.
I will now analyse the tokens for solution in STUCORP and compare their functions and patternings with those for solutions. Of the 118 tokens for solution 73 of these can be classified as ‘Proposing a Solution’ and 45 as ‘Evaluating a Solution’. In the category of ‘Proposing a Solution’ the majority of the tokens for solution, 54 out of 73, occur in the Body of the reports, whereas only 11 and 8 tokens are found in the Introduction and Conclusion, respectively. One common function of phrases with solution in the Introduction is the specification of criteria: In the economic aspect, the recommended solution should be cost-effective… The solution should be financially supportable.
The main reason why the majority of tokens for solution occur in the Body of the reports is that several solutions are enumerated, one by one, using such phrases as the following: Another solution is to mass production of those common dishes. The first solution is to freeze the tuition fee. One solution is that student helpers may check the machine more frequently.
However, nine of the tokens for solution in the Body also refer to the criteria to be met, which overlaps with the function found in the Introduction: The solution should have some flexibility in the structure.
The eight tokens in the Conclusion sections either have a summarising or deductive function, as shown below: Increasing the resources is obviously a solution to the problem. …so we highly recommend CCST to consider this solution in order to…
In the Conclusion, it is to be noted that the Theme/Rheme patterning is different. Whereas in the Introduction and Body sections the majority of the tokens for solution are thematised, mainly for the reason that they are preceded by some kind of determiner or enumerative adjective, the tokens for solution are found in Rheme position in the Conclusion. 45 of the tokens for solution can be classified under the category of ‘Evaluating a Solution’ and of these the majority (35) are found in the Body of the reports, with only two tokens in the Introduction and eight in the Concluding sections.
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Five of the tokens for solution are not evaluated adjectivally, but by other grammatical constructions such as adverbs and verbs acting as two-way signals for the Problem-Solution pattern: This solution can effectively reduce the travelling time …
The remaining 40 tokens for solution collocate with various adjectives, the most common being feasible (14), best (6) and possible (4). In fact, there are 18 instances of a superlative form of an adjective being used, which is not surprising given that several possible solutions are discussed in the Body of the reports. Out of the 35 tokens for solution in the Body sections, 11 carry a negative evaluation, indicating rejection of a proposed solution, e.g.: …alternative 2 is not a technically feasible solution to the problem. If we have a look at the rubrics in Appendix 3-2, we can see that this is actually a response to one of the guiding questions (no. 2). In this respect, see Lea and Street (1999) who consider the relationship of other sites of textual practice such as guidelines for dissertation writing to writing processes and practices in the academy. The Theme/Rheme patterning for solution in the ‘Evaluating a Solution’ category is exactly the same as that in the ‘Proposing a Solution’ category. Solution is found in Theme position in the Body of the reports, e.g. This solution is financially and technically feasible, but occupies Rheme position in the Concluding section, e.g. We find that the installation of surveillance camera in laboratories is a good solution. This analysis of solutions and solution in STUCORP has shown that different forms of a lemma not only have a different colligational patterning, but also that the same lemma can pattern differently depending on which part of the report it is used in.
Analysis of recommended The table below presents a breakdown of the number of tokens for recommended and proposed across the adjectival and various verbal categories outlined in the previous section. In STUCORP there are 58 tokens for recommended, of which only one is used as part of a sub-heading. Of the remaining 57 tokens, 40% occur as a premodifying adjective, and 60% as some kind of verbal form, which are analysed below according to their lexico-grammatical patternings.
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Table 8-2. In-text tokens for the adjectival and verbal categories of recommended and proposed in STUCORP Grammatical category
Recommended No. of tokens % of total
Proposed No. of tokens % of total
Premodifying adjective Impersonal passive Subject + passive Active Clause construction Total
23 8 17 5 4 57
45 2 16 10 3 76
40% 14% 30% 9% 7% 100%
59% 3% 21% 13% 4% 100%
Recommended as premodifying adjective Recommended occurs 23 times as a premodifying adjective and its use is restricted to collocation with a narrow range of nouns in STUCORP, with the most salient being ‘solution’ (12) and ‘system’ (9). This is a very similar use to that found in PROFCORP where recommended was also found to collocate with a few specific nouns but ones related to monitoring and auditing. An examination of these 23 noun phrases shows that 13 occur in some kind of causal relation sentence. One pattern to emerge from the data is a concluding statement of a Grounds-Conclusion type, where an inference is made based on previous analysis: Hence, the data implies that the recommended solution is accepted by various groups of people in the community. Therefore, the recommended system is more economical than the present one.
In contrast, it was found, that in the PROFCORP data the most common cause relation in which recommended was found as a premodifying adjective was Condition-Consequence. Contextual parameters therefore play a role in determining the type of causal relation in which recommended is found as in PROFCORP the focus was on ‘requirements to be met’ whereas in STUCORP the topic is a ‘general evaluation’ of the recommended solution.
Recommended in impersonal passive Eight of the 57 tokens for recommended are found in an impersonal passive construction (see Table 8-2). In percentage terms this is fewer than the tokens for recommended in PROFCORP, but this difference can be accounted for by the fact
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that there are more interpersonal markers with the use of active voice in STUCORP (e.g. we make some recommendations…). All these eight tokens for recommended are involved in the Grounds-Conclusion causal relation. In four of the cases this relation is explicitly signalled by adverbs such as ‘therefore’ and ‘thus’, e.g.: Therefore, it is strongly recommended that a urgent development programme on …should be carried out. Thus, it is recommended that CCST should provide more hardware support to that platform.
However, in the other four cases, as I have argued in the previous section in the discussion of this grammatical construction in PROFCORP, the Grounds-Conclusion relation, while not explicitly signalled, is implied in the text by virtue of the positioning of this lexico-grammatical patterning in the overall discourse. For example, the sentence below occurs in the last part of the report and arrives at a conclusion based on analysis of evidence provided in the Body of the report. It is recommended that those large laboratories should install this system in order to safe guard the precious properties inside.
In this respect, the student use of recommended in impersonal passive, when it occurs, is similar to the professional use.
Recommended in passive + subject construction The passive + subject construction is used 17 times and accounts for 30% of the tokens for recommended in STUCORP, which is very similar to its percentage distribution in this grammatical category in PROFCORP (34%). It is in nouns as subject where the main differences between STUCORP and PROFCORP lie. It was noted in PROFCORP that one category of these subject nouns comprised nominalisations (e.g. The utilisation of quietened equipment is recommended…). However, such nouns, which very often happen to be grammatical metaphor nouns, were not found in this data. In a few cases, students had used a gerund (e.g. installing) where a grammatical metaphor (e.g. Installation) could have been used instead, e.g.: Installing computer terminals is then highly recommended over building a new computer barn.
A cross-check with the Applied Science component of the BNC reveals that installation occurs more frequently (448 tokens) than installing (153 tokens). Although
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installing is acceptable usage, the BNC data do suggest that there is a preference for the nominalisation form over the gerund for this particular verb.
Recommended in active voice There are only five tokens for recommended in the active voice, which are all in the past tense. However the use of the past tense differs from that in PROFCORP where it refers to recommendations made in a previous document and occurs in the Introduction section of the reports. In STUCORP, recommended is found in the Body of the reports where it is used for reporting respondents’ opinions of the survey questions.
Recommended in other clause constructions Only four tokens for recommended were recorded as part of other clause constructions, of which three were reduced clauses. There were no examples of the phrase ‘as recommended’, which was found in PROFCORP as an intertextual device, referring to a previous study or report (e.g. …as recommended in EPD Contaminated Spoil Management Study…).
Analysis of proposed There are 84 tokens for proposed in STUCORP, eight of which function as part of a sub-heading, combining with ‘system’ in five cases. The 76 tokens found in the text of the reports are analysed below according to the grammatical categories outlined in Table 8-2.
Proposed as premodifying adjective The majority, 45 out of the 76 tokens of proposed (i.e. 59%), fall into this category. Likewise, in PROFCORP the majority of the tokens of proposed were also found in this category. A comparison with the tokens for recommended used as a premodifying adjective in STUCORP reveals, however, some differences in frequency and usage. First, proposed occurs more frequently than recommended in STUCORP, with 45 tokens recorded for proposed as opposed to 23 for recommended. The reason for this may be that recommended is mainly used with only two nouns: solution (12) and system (9). Proposed, on the other hand, is found
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to occur with a greater range of nouns (e.g. modification, policy, design, activity, strategy, process, scheme). Not only are there differences in collocation between proposed and recommended in STUCORP, but also differences in their functions in the overall discourse. Whereas it was noted that collocations with recommended were often found in a Grounds-Conclusion type of relation in the latter part of the reports, the collocations with proposed were mainly distributed between the Introduction and Methodology sections of the report (cf. Gledhill 1995, 2000).
Proposed in impersonal passive There is only one example of the lexico-grammatical patterning it is proposed that… and one example of proposed in the impersonal passive followed by a Purpose clause in the Rheme part of the sentence: It is proposed to increase the number of credits for Final Year Project to 6 to solve the problems mentioned above.
Proposed in subject + passive construction An examination of the 16 tokens for proposed in the passive + subject construction shows that of these are all in Rheme position, with two of the tokens preceded by a Purpose clause as Theme, and 13 by a textual theme, as in the following examples: To address the stated problem a censorship system is proposed… Therefore, the following recommendations are proposed.
Although the above patterning is very similar to that found in PROFCORP, several of the sentences are rather awkward sounding because of the subjects of the sentence. The patterning ‘it is proposed that …’ would sound more native-like in the sentences below: The new convenience store is proposed to open from 2pm to 2am. 10 sets of computer terminals are proposed to be placed in Academic Concourse.
In PROFCORP it was found that this construction employed nominalisations in the form of grammatical metaphor nouns, e.g. ‘utilisation’, ‘option’, but such nouns were lacking in student writing.
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Proposed in active voice Again, there are very few tokens of proposed in this category, but one or two points merit a mention. All of the 10 tokens take ‘we’ as the subject. Proposed is mainly found in the Conclusion section of the reports where it always signals a Grounds-Conclusion relation: Therefore, we proposed that CCST should make an announcement to the public to ask the user to be considerate …
However, proposed also occurs in the Introduction section, setting out the background, e.g.: We have proposed four different schemes to be evaluated.
These learner writers are therefore making a distinction between recommended and proposed in the active, as recommended is reserved for reporting the respondents’ opinions in the Body of the reports and none of the five tokens for recommended takes ‘we’ as the subject. Moreover, ‘we’ is never found with either recommended or proposed in the active in PROFCORP. Either the impersonal passive is used or the passive + subject, both of which seem to have the effect of distancing the writer from the recommendations presented in the environmental reports, and can therefore be viewed as typical, formulaic writing style for these kinds of reports in the Hong Kong context.
Proposed in other clause constructions There are only three tokens for proposed where the learner writers have attempted to use it in the form of a reduced relative clause. Two of these are used with an agent (...the FYP topics proposed by the lecturers), but the other one below sounds distinctly odd, and would be more natural substituted by a premodifying adjective (e.g. ‘since the proposed smart card…’). … since the smart card proposed would be accepted by all the photocopiers on campus…
A comparison with the analysis of the tokens for recommended and proposed as reduced relative clauses in PROFCORP reveals that these are always accompanied by some form of postmodification, usually a prepositional phrase, e.g. …controls recommended in this report….
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I now turn to an analysis of one Evoking item and see how it compares across PROFCORP and STUCORP.
Analysis of implementation There are six evoking items in PROFCORP, which occur as keywords in four or more reports. Out of these implementation has been chosen for analysis because it is the only one of the Evoking items, which also occurs in STUCORP (44 tokens) and also because it is the Evoking item which has the most general meaning, as explained previously. The distribution of the tokens for implementation across the functional categories in STUCORP is quite different from that in PROFCORP. First of all, out of the 44 tokens for implementation in STUCORP 17 are used as headings or subheadings, whereas only three out of 133 tokens of implementation in PROFCORP were used in this way. However, this is not surprising as, in accordance with the assignment guidelines, students are expected to discuss various implementation issues of their proposed recommendations. Of the remaining 27 tokens, though, only three can be classified as ‘Evaluating a Solution’, with 24 of the tokens belonging to the ‘Proposing a Solution’ category. This is quite a different distribution pattern to those in PROFCORP where half of the 130 tokens of implementation could be classified as ‘Evaluating a Solution’. Most of the 24 tokens in the ‘Proposing a Solution’ category are found in the Body section, elaborating on implementation of the proposal: The actual implementation is briefed as follows: firstly, the Department announces all FYP topics proposed by the lecturers.
Of the three tokens for implementation which are involved in evaluation, all employ the interpersonal marker ‘we’, which was never used to introduce an evaluation in PROFCORP. Moreover, only one of these phrases with implementation resembles the second type of lexico-grammatical patterning with a two-way signalling verb discussed in the previous section, as given below: ...we suggest that the implementation of a formal laboratory safety course is a necessity to alleviate the current situation.
Given that one of the main objectives of these recommendation reports is to persuade the reader of the value of the project, it would seem that students are unaware of highlighting the benefits of their proposal through the two causation-related lexico-grammatical patternings which were very much in evidence
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in the PROFCORP analysis. However, by searching on implement* in STUCORP I found that in a few cases, students did attempt to give a positive evaluation of their proposed plan, but in a very clumsy way. To illustrate this point I will take the following typical sentence from STUCORP, which although grammatically correct, is ‘student writing’ and not ‘proper writing’, as one of my students put it. (a) Implementing our proposed changes is also highly welcome for students and outside firms and we can have a more effective curriculum preparing students for their future.
We can see that the above is a rather wordy, long, complex sentence. In fact, Hinkel’s (2002) corpus-based research of students’ academic writing has revealed this same overuse of phrase level coordinators such as ‘and’ and ‘but’. A change in the sentence structure of the above example to either of the two following alternatives, modelled on structures prevalent in PROFCORP, gives us the following: (b) Implementation of our proposed changes, which would also be highly welcomed by students and outside firms, would ensure a more effective curriculum to prepare students for their future. (c) With the implementation of our proposed changes… our curriculum would be more effective.
Now, if we compare the student writing in (a) with the suggested reformulations in (b) and (c), we can see that the student writing is more reminiscent of spoken rather than formal written language. By way of example, Halliday (2002) gives the following sentence as an instance of written language with its spoken “translation”, so to speak. Investment in a rail facility implies a long term commitment. (written) If you invest in a facility for the railways you will be committing [funds] for a long time. (spoken equivalent)
Halliday (2004) has remarked that a corpus of written language is set up lexically, and is therefore much easier to analyse. On the other hand, spoken language tends to favour the grammatical over the lexical with long clause complexes occurring in speech, as in Halliday’s example above, and also in my example of student writing. Because of its grammatical intricacy, Halliday maintains that a corpus of spoken language is more difficult to analyse than a corpus of written data. This is an extremely interesting observation, which has been borne out by the above data in STUCORP. It also suggests that the distinctions between writing and speaking may not be so clear-cut in cases of learner corpora of written data and that in
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some instances where learner corpora display features of spoken language, they could be viewed more as a ‘hybrid’ corpus of written and spoken language.
Conclusion The most striking observations regarding the lexico-grammatical patternings of the key words for the Solution element in STUCORP is that they rely quite heavily on the metalanguage of the rubrics and guidelines for the project, as has been noted in several places in the foregoing analysis. Students did demonstrate writing proficiency, though, in their use of topic-like sentences and overall summary conclusions. There was also far more interpersonal use of the pronoun ‘we’, e.g. we will provide recommendations … in the student reports, which was not in evidence in PROFCORP with a similar proposition being expressed by the impersonal passive, e.g. it is recommended / proposed that …. However, due to the different ethnographic situations in which the STUCORP and PROFCORP reports are constructed (see Chapter 3) these different registers are entirely appropriate.
chapter 9
General conclusions and implications for pedagogy
In this concluding chapter I will first consider the major findings revealed by the analysis of PROFCORP in light of the general objectives outlined at the end of Chapter 2. I will then summarise the main similarities and differences between PROFCORP and STUCORP from a discourse-based perspective and also at the sentence-level to highlight specific features of apprenticeship writing. Finally, I will discuss the pedagogic applications of these findings.
Some principal findings from PROFCORP It is now well established in the literature that language is made up of interlocking phraseological units, i.e. lexico-grammatical patterns (see Hoey 1991, 2005; Sinclair 1991; Hunston 2001; Hunston & Francis 2000; Stubbs 1996), although what proportion of language constitutes these units is open to debate. Where this book has attempted to advance the field of lexico-grammatical patterning is through examining this language phenomenon within a discourse-based framework of notions and functions for a specific rhetorical pattern, namely the Problem-Solution pattern. A review of some of the key findings in PROFCORP, which seek to answer the questions posed in Chapter 2, follows. As far as the Problem element is concerned, phrases signalling this element have been shown to be heavily involved in causal relations, most notably the Reason-Result and Means-Purpose relation. What is most significant about the markers of these causal relations is not that they are typically some type of connective, e.g. ‘As a result…’, ‘Therefore…’, but are overwhelmingly lexical in nature in the form of explicit and implicit causative verbs. Another significant finding is that prepositions, especially ‘with’ and ‘from’, also seem to be acting as signals of causality, as does the more mitigating phrase ‘associated with’ (as shown in Chapter 7). It has also been suggested that the status of a lemma can change depending on whether it enters into a causal relation or is non-causation based. For example, problem, a Vocabulary 3 item which is viewed as a common A-Noun, has this
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function mainly in causation-related phrases (see Chapter 7). Another keyword signal impacts investigated in Chapter 5 seems to be functioning as a synonym of problems in causation-related phrases, but changes its status to one of hyponym in relation to problems when it occurs in non-causation phrases. These preliminary findings indicate that more research into looking at how discourse-based notional categories affect the status and the relationship of a lexical item with other items would be worth pursuing. In the analysis of key words for the Solution element, the systemic functional category of grammatical metaphor noun (e.g. implementation) was found to be instrumental in signalling the Solution element in PROFCORP. Such nouns, which are a feature of formal scientific writing, were found to occur in different lexicogrammatical patterns depending on their overall function in the discourse. Aspects which concern genre analysts, such as intertextuality (the linguistic traces of other texts) and interdiscursivity (rhetorical conventions borrowed from other texts), have also been touched on (Flowerdew 2008a). It was suggested in Chapter 5 that ‘associated with’ was used as a hedging device for expressing causality in view of the fact that this verb phrase with a negative semantic prosody was found to occur in texts from all 23 companies, thus indicating it was a rhetorical feature of this particular genre, and possibly of science writing in general. Examination of the keyword signals from a grammatical basis within the broad function of ‘Proposing a Solution’ has uncovered the intertextuality of the PROFCORP documents and their relationship to other related documentation through such phrases as recommendations made in… and the use of a specific body or organization with a keyword verb in the present perfect, e.g. The EIA has recommended that …. Such ethnographic considerations alert us to the fact that in order to fully and accurately “interpret” the lexico-grammatical patternings, the role that contextual features, outlined in Chapter 2, play in shaping the discourse has also to be taken into account. For this reason, Widdowson (1998, 2002) maintains that corpus data are but a sample of language, as opposed to an example of authentic language, because it is divorced from the communicative context in which it was created; ‘the text travels but the context does not travel with it’. This is an important observation and can create dilemmas for the analyst in corpus interpretation. This is where I see the value of working with small-scale specialized corpora such as the kind investigated in this book and also those reported on in Ghadessy et al. (2001) where the analyst is probably also the compiler and does have familiarity with the wider socio-cultural dimension in which the discourse was created (Flowerdew 2004a). It also means that ultimately doing small-scale corpus linguistics is different from doing large-scale corpus linguistics. Investigations of large-scale corpora can give us valuable insights into broad-based collocational
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and colligational patterning: for example, a trawl through the 246 tokens of ‘recommendations’ in the Applied Science component of the BNC reveals that ‘recommendations’ frequently occurs with the verb ‘make’ but in three very different lexico-grammatical patternings: To highlight the salient points, the following recommendations are made. We have made various recommendations for strengthening… This document makes several specific recommendations on pay.
Finer tunings of lexico-grammatical patternings I have referred to as lexical colligations. But which grammatical company that the collocation ‘recommendations + make’ keeps for a particular context may only be available to an analyst who is familiar with the socio-contextual features of a small corpus and not recoverable from the concordance lines drawn from a large-scale corpus. Corpora are thus delimited by ethnographic considerations such as intertextuality, interdiscursivity and registerial constraints, involving choices like interpersonal ‘we’ vs. impersonal passive, or technical vs. non-technical lexis, as well as text-type (Problem-Solution pattern, in this case), which themselves are all factors in determining the specific lexico-grammatical patternings.
Expert vs. apprentice writing Investigation of the lexico-grammatical patterning of key words for the Problem and Solution elements in STUCORP has uncovered areas where students show themselves to be proficient and areas where they are deemed less proficient. The analyses in Chapters 7 and 8 have led me to conclude that students seem to be quite adept at structuring their overall argument within the Problem-Solution based pattern. They show mastery of lexico-grammatical patterning at the macrostructure level for using topic-like sentences for introducing the problems, e.g. There are several problems…, and then enumerating these in follow-up supporting sentences, e.g. The first problem is… (Chapter 7). The analyses also show that the development of the argument unfolds quite logically. This is apparent from Tables 4-1 and 4-2 showing the keyword Inscribed and Evoking items for the pattern and also the investigation of the key word need in Chapter 7. First, students establish the existence of a problem. Moreover, this Problem element is very often focused on some kind of shortcoming, which explains why insufficient surfaces as a keyword Evoking item, e.g. … number of available computers in barns becomes really insufficient. Then either there is a kind of prefacing statement to Hoey’s Recommended Response stage (see Chapter 1), e.g. …the number of
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machines cannot meet the need of students; or, we have a Recommended Response, e.g. Therefore, there is a need to provide more payphones. However, students’ use (or possibly overuse?) of topic sentences can be explained by the fact that the course materials in the first year overtly teach this signalling aspect of writing. Also, the report writing guidelines for this project contain several expository-type exercises for structuring the content of the report, which provide students with the basic macrostructure (see Appendix 3-1). It thus appears that student have assimilated into their reports the metalanguage given in the writing rubrics and guidelines, which has been shown to permeate the discourse of the STUCORP reports, one very common overused patterning being variations of ‘solutions to the problem’. The influence of input material here, in the form of writing guidelines, is therefore an important consideration in analyses of learner corpora. Other researchers (see Milton 2000; McEnery & Kifle 2002) have also noted the influence of coursebook material, specifically the expressions of doubt and certainty, on student writing. Sentence-level formal errors have been exposed, one such error being the misuse of ‘It’, which is specifically related to the interlanguage of Hong Kong Chinese learners of English; for instance, the substitution of existential ‘there’ by ‘It’, e.g. It is no need to explain. However, what seems to distinguish apprentice writers from professional writers is not so much these sentence-level formal errors, but rather other types of deficiencies related to expressing causal relations, as summarized below. Although students’ writing may be grammatically correct for the most part, this corpus-based analysis has uncovered several areas where students’ writing ‘doesn’t sound quite right’, for want of a better expression, or unidiomatic because they lack the necessary lexico-grammar for expressing their ideas. In many cases, students seem to be circumventing their lack of appropriate patterns by using what language means they have at their disposal and coming up with phrases where the essential meaning can be understood, but ‘we wouldn’t express it like this is English’, such as in the phrase …have a very good policy against the problem. Sentences such as the one above would seem to arise from the fact that the student writing was found to be wanting especially in the area of verbs, with students exhibiting a very narrow range of these verbs, confusing cause/effect with result/effect verbs and displaying inadequate command of ergative verbs for the Problem element. The verbs used for expressing the Solution element were also, on occasions, registerially inappropriate, e.g. get rid of. Grammatical metaphor nouns (e.g. implementation), which are a feature of formal science writing and predicted certain ‘two-way’ signalling verbs in PROFCORP linking the Problem and Solution elements, of the pattern: implementation of + Solution would alleviate + Problem, were largely absent in STUCORP. Lack of familiarity with this
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pattern, and indeed others involving nominalizations, could largely explain why students writing resembled more spoken language in places with ‘and’ used to join clause complexes. In sum, the types of deficiencies observed in student writing are not so much sentence-level formal errors, but rather indicate the lack of an appropriate grammar system and vocabulary range which play a role in the co-construction of meaning through the blending of collocational and colligational features of language in the type of discourse under investigation in this book. This is the kind of student writing de Beaugrande (2001: 10) is singling out when he writes ‘Our major problem is not so much bad English or incorrect English, as is often lamented, but rather insufficient English’.
Pedagogic implications and applications of findings Most of the language exercises in coursebooks on technical writing focus on sentence-level grammar errors such as use of tenses and formation of the passive voice. While not denying the value of such exercises, the analyses in Chapters 5, 6, 7 & 8 have demonstrated that more language work needs to be devised on lexicogrammatical patterning in order to bring apprentice writers up to the level of professional writers. It is not just a question of using the passive voice correctly, but as the analyses have revealed, using appropriate lexicogrammatical patternings with consideration of various contextual and situational features of the discourse for the notions and functions one wishes to convey: causality and proposing / evaluating a solution being the respective categories under investigation in this book. Such examples would be the combination of appropriate nominalizations with the passive voice or the juxtaposition of prepositions with grammatical metaphor nouns e.g. With the implementation of…to signal the Solution and Evaluation elements, particularly in a concluding section of the text. The findings and implications for pedagogy derived from applied linguistics work in phraseology (Groom 2005; Hunston 2003; Jones & Haywood 2004; Sinclair (ed.) 2004; Wray 1999, 2000) are now beginning to filter through into the teaching community (see Flowerdew 2001, 2002; Tribble 2001; Willis 2003). In a state-of-the-art article on ESP, Belcher (2006) remarks on the increasingly mainstream role that corpora are now playing in enhancing ESP literacy. Needless to say, one of the major challenges for textbook writers in the next few years will be how to exploit and “translate” corpus-based findings of discourse-based phraseological units into comprehensible input for learners. In any case, Widdowson (1998, 2002) views the transference of corpus data to pedagogy as problematic due to the decontextualised nature of corpus data and questions how such data
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can be transformed from samples to examples of language, i.e. how can students authenticate the corpus data to suit the socio-cultural and linguistic parameters of their local environment? However, other linguists would seem to disagree that this is a major hurdle to be overcome, with Sinclair (2002) proposing some kind of ‘pedagogic processing’ stage to make the data intelligible to learners and McCarthy (2002) objecting on the grounds that Widdowson underestimates the ability of students to change samples into examples. Again, this is where I see the value of exploiting small ‘localised’ expert corpora for pedagogic purposes; the more the corpus draws on contextual features from the students’ own socio-cultural environment, the easier it should be for the teacher to act as a kind of mediating ‘ethnographic specialist informant’ of the raw corpus data, thereby authenticating the data for classroom use to fit the students’ reality. Another way forward in this area is the compilation of local learner corpora, similar to the one described in this book, as advocated by Mukherjee (2006), Mukherjee & Rohrbach 2006) ‘…the exploration of learner data by the learners themselves will motivate many more learners to reflect on their language use and thus raise their foreign language awareness’. See Nesselhauf (2003, 2004b) for a review of the applications of learner corpora to language teaching and useful suggestions for the exploitation of learner corpora in data-driven learning (DDL). Based on the deficiencies uncovered through a comparison of PROFCORP and STUCORP, below I outline a few suggestions for DDL exploiting various search engines, with reference to some of the points raised in the literature on the corpus-based approach.
Applications of corpora in data-driven learning: Some critical points I have noted the importance of a lexico-grammatical orientation to corpus analysis in this book, and would like to reiterate the application of this perspective to DDL, an approach that is inherent in the tasks proposed by Gavioli (2005) with their focus on collocation, colligation, semantic prosody and semantic preference. However, this approach is not without its problems, which are also raised in Gavioli’s book and summarized by Meunier (2002). Despite their advantages, DDL activities have some drawbacks: they are timeconsuming (because of the interaction, negotiation and research procedure adopted by the students) and also require a substantial amount of preparation on the part of the teacher, who has to predefine the forms that will be focused on and make sure that interesting teaching material is provided. The various learning strategies (deductive vs. inductive) that students adopt can also lead to problems.
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Some students hate working inductively and teachers should aim at a combined approach (see Hahn 2000 for a combined approach). (Meunier 2002: 135)
In the exposition of some lexico-grammatical concordance activities below, I would like to take up, in particular, two other, not necessarily drawbacks, but rather considerations, of a DDL approach. One issue relates to Widdowson’s point that students’ need to authenticate the corpus data to fit the contextual environment of their own writing. In this regard, I will come back to a finding from Chapter 8, namely the lack of grammatical metaphor nouns, e.g. implementation, in student writing (see Mohan & Huxur 2001). I suggested that in a recommendation report proposing modifications to the existing curriculum, student writing could be reformulated along the following lines, linking the solution and evaluation via an implicit causative verb instead of ‘and’. Student writing: Implementing our proposed changes is also highly welcome for students and outside firms and we can have a more effective curriculum preparing students for their future. Reformulation: We would like to suggest that implementation of our proposed changes, which would be highly welcomed by students and outside firms, would ensure a more effective curriculum to prepare students for their future.
The students writing needs to undergo several pedagogic processing stages for reformulation into a more professional type of discourse. First, students need to be reminded of the given and new paradigm operating in this extract. In their context of writing, these proposed changes ‘are highly welcome for students and outside firms’ refers to information already mentioned in a previous sub-section, while the Evaluation element of the proposed changes is new information for the reader. Thus, it would be most appropriate to assign the information previously mentioned to a relative clause. Then, Just The Word, a collocations search engine which provides an interface for the BNC, was used for a search on implementation. This gave the results shown in Figure 9-1 below. However, the collocations for implementation only provide ‘frames’ so the teacher would have to sensitise students to ‘authenticating’ the corpus data to fit their writing environments for it to be appropriate from both a lexico-grammatical and pragmalinguistic point of view. First, students have to be alerted to the fact that when ‘implementation’ collocates with ‘lead to’ or ‘result in’, these verbs tend to be followed by something negative, whereas ‘provide’ and ‘ensure’ usually have a positive semantic prosody. (This type of information can be gleaned by looking at the concordance output.) Second, concordance samples have to be changed
136 Corpus-based Analyses of Problem-Solution Pattern
Figure 9-1. Collocation search for ‘implementation’ in Just The Word
into ‘examples’ to fit the students’ writing environment through mitigation markers such as ‘we would like to suggest...’ and the use of ‘would’ instead of ‘will’. The same type of search could be conducted for ‘problem’, which would throw up those two-way signalling verbs, e.g. alleviate, minimize, relieve, for linking the Problem and Solution elements, which were found to be lacking in student writing. But again, the concordance output would probably have to be ‘authenticated’ for the writing context. Milton (2006: 125) mentions that such kind of programs as the one shown above ‘would turn the notion of appropriation around and point learners to resources where, as in Bakhtin’s (1981) concept, they would be the ones appropriating the usage of more experienced writers of the L2’. The example of implementation shows that students need to both ‘appropriate’ and ‘authenticate’ the corpus data for their own writing purposes. While examining the collocations shown in Figure 9-1, students could be encouraged to browse the other collocations on their own, in the spirit of Bernardini’s (2002, 2004) philosophy of ‘The learner as traveller’, alighting on aspects which were of potential interest to them in a type of discovery or serendipitous learning. Although this type of incidentalist learning has been criticized by Swales (see Swales 2004; Lee & Swales 2006) and may be rather time-consuming and lead students down a few blind alleys, some exploratory work shows that this trial-anderror approach does seem to have a motivational appeal (Flowerdew 2008c). Another consideration concerns the interpretation of the corpus data by students, an issue which does not seem to have been discussed much in the litera-
Chapter 9. General conclusions and implications for pedagogy 137
ture on pedagogic applications. As Hunston (2002: 65) notes ‘Concordance lines present information; they do not interpret it. Interpretation requires the insight and intuition of the observer’. The ‘interpretation’ of the data by the analyst can be seen as paralleling the inductive learning approach commonly associated with DDL in which learners extrapolate rules from their ‘readings’ of the concordance lines. Carter and McCarthy (1995) expand on this exploratory approach, terming it the ‘three Is’ (illustration-interaction-induction), with ‘illustration’ meaning examining corpus data and ‘interacting’ discussing and exchanging opinions on the data. With this consideration in mind, I would like to examine ergative verbs, which have been shown to be another area of difficulty for students. In the sentence below ‘with’ can be seen as a causative marker, followed by the effect in the second part of the sentence. With a very crowded schedule, students’ level of motivation was decreased.
Those change-of-state verbs such as ‘decrease’, ‘increase’ and ‘develop’, which have three possible voices (active; passive; middle (or ergative), pose particular difficulty for students both in terms of grammatical complexity and search strategies. As noted in Chapter 7, Celce-Murcia (2002: 147) notes that advanced level students tend to overpassivise such verbs, using the passive in cases where the ergative should be used, e.g. Over the period of the study the learners’ VOT values for … were decreased, a similar misuse to the example given from STUCORP. However, students have been found to have difficulties in working out inductively the various usage patterns of such ergative verbs from truncated concordance lines in a corpus of reports. Prompting was required from the class teacher to encourage students to look at the wider context in order to notice that it was the subject that determined the voice, thus following Sinclair’s principle of language viewed as ‘extended units of meaning’. In this regard, Celce-Murcia’s observes that ‘With the verbs increase and decrease [the ergative] tends to be used when the inanimate subject is objectively or subjectively measurable (rather than an animate agent/dynamic instrument subject) − both of which favor active voice − or a patient subject − for the passive voice’ (p. 146). With oral prompting from the teacher, students were able to articulate, in their own words, this phraseological tendency, by examining selected concordance output generated by the Word Neighbours concordancing software shown in Figure 9-2 (see Milton 2006; Flowerdew 2008c). The above example shows that a purely inductive approach to interpretation of corpus data may sometimes be too sophisticated for students. Although deductive vs. inductive approaches are usually presented as diametrically opposed,
138 Corpus-based Analyses of Problem-Solution Pattern
You can select a word/phrase and right-click to get definitions, pronunciation, translations, similar words, more contexts, etc.
1 2
Search results for has decreased by (VERB VERB PREP)
Text type
In spite of this, coniferous output has decreased by nearly 8 million m3 and non-coniferous output by nearly 10 million m3 …more On the average the number of iterations needed to solve the problems has decreased by 1:62% and the average time was cut by 8.33% …more
Reports ir-97-099.txt Reports WP-95-113.txt
Figure 9-2. Context search for ‘has decreased’ in Word Neighbours
I would like to suggest that rather, they could be seen on a cline, with the teacher providing hints on the rule in cases where students did not have enough knowledge to interpret the concordance lines by themselves. While Flowerdew (2008c) reports discussion activities where hints are given orally, Milton (2004, 2006) describes a writing tutor program where various types of clues (grammatical, lexical, links to suitable corpora) can be inserted by the tutor. Thus, it may be necessary for some kind of intermediary stage to facilitate interpretation of concordance output when a more inductive approach is used. This discussion of some critical points in DDL has highlighted the fact that raw corpus data may have to undergo a ‘pedagogic processing’ stage with some kind of intervention on the part of the teacher. As I have suggested, this may involve ‘authenticating’ the data to fit the students’ context of writing as Widdowson advocates, or may involve providing various types of clues, either verbal or written, to aid students’ in the interpretation of concordance lines.
Overall conclusions In the Festschrift volume in honour of John Sinclair (Baker, Francis & TogniniBonelli 1993) and the Festschrift volume in honour of Michael Hoey (Scott & Thompson 2001), even though these volumes are published eight years apart, we can still see the predominant role that linguistic patterns play in the disentangling of corpus data. If we consider patterns / patternings as a ‘headword’ subsuming ‘entries’ such as phraseology, collocation and colligation, we can see this notion is like a leitmotif running through many articles in both these volumes (cf. Francis 1993; Hunston 2001).
Chapter 9. General conclusions and implications for pedagogy 139
This book has explored some of the central notions of patterns expounded in these volumes with specific reference to the Problem-Solution pattern in a professional and apprentice corpus. Moreover, an attempt has been made to examine the lexico-grammatical patterning of the Problem-Solution pattern from a more textlinguistic perspective, outlined in Chapter 2. Categories from systemic functional grammar, such as Inscribed and Evoking lexis, grammatical metaphor nouns and Theme/Rheme patterning, have been a useful aid for analysis of the data. At the same time, recurring patterns have also provided evidence for the discursive practices of the particular genres under investigation. Comparisons of certain features in STUCORP with PROFCORP and the BNC have revealed key areas where students’ deficiencies in writing lie. It is hoped that this corpus-based lexico-grammatical analysis of the Problem-Solution pattern in two corpora has made a contribution to this ever-developing field and suggested some worthwhile avenues for future exploration.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
Backfilling of South Tsing Yi and North of Lantau MBA’s Centralised Incineration Facility for Special Wastes Island East Transfer Station: Dredging of the WENT Fairway Lantau and Airport Railway Main Drainage Channels for Ngau Tam Mei, Yuen Long and Kam Tin (Jan. 1995) Main Drainage Channels for Ngau Tam Mei, Yuen Long and Kam Tin (June 1996) Natural Gas Supply to Black Point Power Station Proposed Aviation Fuel Receiving Facility at Sha Chau Reclamation and Servicing of Tuen Mun Area 38 for Special Industries Relocation of Hong Kong Oxygen Site from Junk Bay to Kwan O Industrial Estate Central and Wan Chai Reclamation Development (Phase I) Proposed Extension to the Hong Kong Convention and Exhibition Centre Central and Wan Chai Reclamation Development (Phase II) Low-level Radioactive Waste Storage Facility Lantau Port Development Stage I: Container Terminals 10 and 11 New Airport Master Plan
Title
Appendix 3-1. Reports in PROFCORP
Appendices
1_ERM (ERM, HK Ltd.) 2_ ERM 3_ERM 4_ERM 5_ERM 6_ERM 7_ERM 8_ERM 9_ERM 0_ERM 1_MAU (Maunsell) 2_MAU 3_MAU 4_MAU 5_MAU 6_MAU
* Filename (Consultant)
2,548 4,609 1,634 5,265 4,118 5,311 2,409 6,452 2,195 1,612 2,587 2,340 3,219 4,916 4,119 11,947
Words
17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42.
New Airport Master Plan (EIA summary) Tseung Kwan O Development – Roads, Bridge and Subways Tseung Kwan O Development – Improvements to Ying Yip Road Proposed Reclamation at Sham Tseng Four Potential Housing Development Sites Discovery Bay Development Proposed Reclamation and Relocation of United Floating Dock Shiu Wing Steel Mill Tuen Mun Area 38 The Hong Kong Golf Centre Additional Treatment and Water Transfer Facilities for Metropolitan Area Investigation and Development of Marine Borrow Areas Kam Tim Bypass Wan Chai East and North Point Sewerage Route 2 Tai Lam Tunnel and Yuen Long Approach - Southern Section Route 3 Tai Lam Tunnel and Yuen Long Approach - Northern Section Shek O Quarry Casting Basin Tai Po Development: Formation and Servicing of Area 12 Permanent Site for mid-stream operation of Stonecutter’s Island Route 5 Section between Shek Wai Kok and Chai Wan Kok Strategic Sewage Disposal Scheme (Stage I) Dredging of the Anchorage Area for Stonecutter’s Island Naval Base Green Island Reclamation Public Dump Restoration of North-West New Territories Landfills Tuen Mun Port Development Study Agreement No. CE 52/94 West of Sulphur Channel Marine Borrow Area Improvement to Castle Peak Road from Siu Lam to So Kwun Tan
(continued) 7_MAU 8_MAU 9_MAU 1_AXS (Axis) 2_AXS 3_AXS 4_AXS 5_AXS 6_AXS 1_BIN (Binnie) 2_BIN 3_BIN 4_BIN 1_CES (CES) 2_CES 3_CES 4_CES 1_MOT (Mott McDonald) 2_MOT 3_MOT 4_MOT 1_SWK (Scott, Wilson, Kirkpartick) 2_SWK 3_SWK 4_SWK 1_FRA (Peter Fraenkel)
2,720 1,892 940 1,976 1,779 6,571 4,436 3,707 5,743 1,180 1,790 1,695 6,324 4,753 3,970 3,541 2,256 5,421 1,882 4,575 2,543 3,025 6,733 4,453 1,512 4,413
142 Corpus-based Analyses of Problem-Solution Pattern
PWP Item 454th: Lung Cheung Road Flyover Reclamation Works for District Open Space and GIC Facilities Restoration of Shuen Wan Landfill East Kowloon Sewerage Improvements and Pollution Control Lung Cheung Road and Ching Cheung Road Improvements Lantau Port and Western Harbour Development Studies Outlying Islands Refuse Transfer Facilities Studies Tolo Harbour Effluent Export Scheme Sheung Shui Slaughter House Proposed Extension to Tseung Kwan O Landfill Stage I Extension Ozone Depletion – the latest findings South East New Territories (SENT) Landfill Lantau Port Development Stage I Hung Hom By-pass Shenzen River Regulation Project Port Development Strategy 1991 Mingai Dyeing and Printing Factory Limited Design and Construction of Smithfield Extension
* The name of the Consultants is given in brackets after the first file.
43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60.
(continued) 2_FRA 1_MOU (Mouchel) 2_MOU 1_PHH (PHH Consultants) 2_PHH 1_APH (APH Consultants) 1_ASP (Aspinall) 1_BAL (Balfours) 1_ECL (Enviro-Chem Eng.) 1_EPD (Envir. Protection Dept.) 1_FOE (Friends of Earth) 1_GVL (Green Valley Landfill) 1_HAL (Halcrow) 1_HIG (Highways) 1_LEA (Lead) 1_PLA (Planning Dept.) 1_PRO (Productivity Council) 1_PYP (Pypun) Total number of running words
4,127 3,506 2,931 4,180 4,400 6,657 5,755 6.911 1,386 5.440 2,367 3,189 3,476 4,479 4,226 3,212 2,843 1,900 226,97
Appendices 143
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
Improving computer Facilities Computer barns in the HKUST Information report on CCST computer barns Computer barns Investigation into the usage of computers in CCST computer barns Installing computer terminals in UST campus Situation in computer barns Service provided in computer barns Usage of modem lines in UST Printing service in CS laborarory CCST facilities Dial-up service Internet services after graduation Computerisation of sports facilities booking system On-line booking system for sports facilities Chemical Engineering design project Review of Chemical Engineering degree Organisation of study tour for CPEG students Final year projects Opinion on compulsory H&SS courses Proposal for establishment of a Humanities self-learning centre Review of continuous assessment scheme
Title
Appendix 3-2. Reports in STUCORP
1_CS (CCST) 2_CS 3_CS 4_CS 5_CS 6_CS 7_CS 8_CS 9_CS 10_CS 11_CS 12_CS 13_CS 14_CS 15_CS 1_COU (Courses) 2_COU 3_COU 4_COU 5_COU 6_COU 7_COU
* Filename (topic)
1,868 3,091 2,814 2,740 3,879 2,355 3,695 1,862 3,346 3,100 2,183 2,361 2,987 1,654 2,578 3,070 3,516 2,405 3,527 2,405 3,013 3,513
Words
144 Corpus-based Analyses of Problem-Solution Pattern
23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48.
Investigation of sports facilities Physical education programmes Sports programme Student participation in physical education programme Usage of sports facilities Sports facilities in UST Constructing a water sports centre Putonghua courses Demand on Putonghua courses in UST Need for Putonghua courses Attitudes towards language study Language skills Catering service Survey on catering services Research on catering services Evaluation of noodle shop Course registration system Students’ attitude towards present course registration system Course registration Investigate the current course registration and add/drop procedure Introducing new student credit card Credit cards Investigation of credit card usage Feasibility of using Smartcard in HKUST campus Survey on students’ and lectures’ attitudes towards cheating Academic dishonesty
(continued) 1_SPO (Sports Facilities) 2_SPO 3_SPO 4_SPO 5_SPO 6_SPO 7_SPO 1_LAN (Language Courses) 2_LAN 3_LAN 4_LAN 5_LAN 1_CAT (Catering services) 2_CAT 3_CAT 4_CAT 1_REG (Course registration) 2_REG 3_REG 4_REG 1_CRE (Credit cards) 2_CRE 3_CRE 4_CRE 1_CHE (Cheating) 2_CHE
2,646 2,734 2,239 2,972 1,483 3,938 2,344 1,766 3,588 3,796 3,413 2,461 1,968 1,656 2,492 3,106 3,276 4,871 2,851 4,533 2,270 2,722 3,564 2,595 4,200 2,565
Appendices 145
49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74.
Assignment copying in UST UG hall security Security in halls Security and illegal use of lockers Proposal for superstore Establishment of convenience store Convenience store Property theft Evaluation of lab security Thefts from laboratories Investigation on effects of increasing tuition fee Survey on increasing tuition fee Hall places Current situation on hall places for UG students Seriousness of existence of pornography in UST network Pornography on the internet Investigation into laboratory accidents Standard of laboratory safety Intellectual property rights Copyright laws Investigation of library facilities University library Telephone facilities in UG halls Payphones on campus Financial condition of UST students Income and expenditure of UST students
(continued) 3_CHE 1_SEC (Security) 2_SEC 3_SEC 1_STO (Store) 2_STO 3_STO 1_THE (Theft) 2_THE 3_THE 1_FEE (Tuition fee) 2_FEE 1_HAL (Hall places) 2_HAL 1_INT (Internet) 2_INT 1_LAB (Lab safety) 2_LAB 1_LAW (Copyright laws) 2_LAW 1_LIB (Library) 2_LIB 1_PHO (Telephones) 2_PHO 1_SPE (Spending) 2_SPE
2,593 3,341 2,975 1,585 2,916 3,030 3,088 3,775 2,132 2,159 2,884 3,032 1,915 1,893 2,265 4,763 2,995 3,419 2,813 3,215 3,902 2,587 3,048 2,641 2,584 2,353
146 Corpus-based Analyses of Problem-Solution Pattern
Photocopying service Awareness of environmental protection Difficulties in finding a job Comparison of services provided by paging companies Usage of student union print shop Advantages and disadvantages of SU compulsory membership
1_COP (Photocopying) 1_ENV (Environment) 1_JOB (Job hunting) 1_PAG (Pagers) 1_PRI (Printing) 1_STU (Student Union) Total number of running words
1,925 2,529 3,083 2,484 3,379 3,398 228,704
* The topic area is given in brackets after the first filename. NB: Computer barns mentioned in titles 1–8 refer to computer laboratories where 50–100 networked computers are installed. Students use these facilities to complete homework assignments.
75. 76. 77. 78. 79. 80.
(continued)
Appendices 147
148 Corpus-based Analyses of Problem-Solution Pattern
Appendix 3-3. Guidelines for Project Stages of the Project Stage 1: Planning a study – nature of an investigation – identifying a problem or need – scope of an investigation – choosing a topic Stage 2: Methods of data collection Primary sources – making observations – interviewing – designing a survey questionnaire Secondary sources Stage 3: Writing a report – documenting activities (writing a progress report on data collection) – discussing data (reporting and interpreting data) – types of reports (information, feasibility, recommendation)
Scope of an Investigation When you are planning your research project, there are many questions that you may need to consider. These may include: 1. Is there a problem? What evidence do I have that this problem exists? How serious is the problem? 2. What possible solutions are there to an existing problem? Are these solutions technically, economically and socially feasible? 3. Is there a need for a new development? What is the evidence that this need exists? 4. How can an existing need be met? Can it be met in a way that is technically, economically and socially feasible? Obviously, the majority of research projects will not attempt to consider all these questions fully. Instead, projects tend to concentrate on answering one or two questions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Num
THE OF TO IN AND IS STUDENTS THAT A FOR ARE IT WE BE NOT THIS THEY FROM ON HAVE AS BY
19,644 9,571 6,531 5,107 5,102 3,611 3,429 3,235 2,916 2,542 2,080 1,777 1,744 1,741 1,551 1,537 1,493 1,490 1,475 1,408 1,365 1,235
STUCORP (228, 704)
FREQUENCY
THE OF AND TO BE IN WILL A FOR IS ARE FROM ON NOISE AT CONSTRUCTION THAT WITH AS BY SITE IMPACTS
19,826 9,292 8,030 6,392 4,161 3,781 3,019 2,802 2,495 2,358 1,697 1,680 1,666 1,552 1,535 1,476 1,413 1,398 1,350 1,266 1,086 1,080
PROFCORP (226,097)
Appendix 4-1. 100 most frequent words across 4 corpora
THE OF AND TO A IN IS FOR THAT WAS IT ON BE WITH AS I BY AT ARE HE FROM BUT
66,619 32,535 28,623 26,462 21,648 20,965 9,799 9,547 8,469 8,167 7,865 7,277 7,169 7,063 6,504 6,216 5,885 5,602 4,961 4,889 4,683 4,341
BNC (1,080,072) THE OF AND TO A IN THAT I IT WAS IS HE FOR YOU ON WITH AS BE HAD BUT THEY AT
309,497 155,044 153,801 137,056 129,928 100,138 67,042 64,849 61,379 54,722 49,186 42,057 40,857 37,477 35,951 35,844 34,755 29,799 29,592 29,572 29,512 28,958
COBUILD (c. 7.3 m)
Appendices 149
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
CAN THEIR MORE WERE WITH WILL WAS MOST THERE TIME ALSO RESPONDENTS THEM ABOUT SOME COURSES COMPUTER OR OUR COURSE NUMBER ONE HKUST WHICH ONLY SYSTEM
(continued)
1,218 1,192 1,043 1,034 972 969 955 876 872 855 825 819 815 774 758 734 706 688 674 649 625 599 586 586 578 562
AREA WATER THIS ENVIRONMENTAL WHICH QUALITY WORKS IMPACT ROAD NOT BEEN IT WOULD MEASURES AN DURING OR SHOULD HAVE HAS PROPOSED LEVELS MITIGATION DEVELOPMENT STUDY AIR
1,041 958 930 849 845 844 748 745 738 734 716 715 705 702 694 680 677 646 643 615 614 609 581 572 540 509
HAVE THIS NOT YOU HIS WHICH AN HAD THEY OR WERE WILL ALL WE ONE HAS THEIR THERE SAID BEEN SHE HER WOULD IF UP MORE
4,295 4,262 4,221 4,045 4,000 3,703 3,618 3,580 3,486 3,395 3,283 3,107 2,995 2,985 2,929 2,761 2,713 2,598 2,558 2,540 2,485 2,447 2,338 2,168 2,087 2,082
HIS HAVE NOT THIS ARE OR BY WE SHE FROM ONE ALL THERE HER WERE WHICH AN SO WHAT THEIR IF WOULD ABOUT NO SAID UP
26,491 26,113 25,419 25,185 23,372 22,445 21,916 20,964 20,958 20,933 20,354 20,022 19,145 18,916 18,547 18,344 17,446 17,433 16,434 16,160 16,008 14,687 14,547 14,386 14,163 13,552
150 Corpus-based Analyses of Problem-Solution Pattern
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
ALL SO THAN HALL USE AT SHOULD OTHER STUDENT PROBLEM QUESTIONNAIRE SERVICE FACILITIES MAY AN DO DATA IF FIGURE OUT STUDY LIBRARY WOULD HOWEVER THESE QUESTIONNAIRES
(continued)
561 553 553 546 542 537 535 522 510 487 485 452 451 441 437 437 428 428 426 424 414 408 408 406 396 391
MONITORING THESE TRAFFIC WAS WASTE WITHIN MARINE EXISTING AREAS WERE ANY ALL NO ASSESSMENT RECOMMENDED POTENTIAL SENSITIVE OTHER OPERATION DESIGN DREDGING ALSO LANDFILL DUST INTO FACILITY
504 501 492 486 482 477 467 463 450 445 439 426 425 422 420 409 390 387 380 373 360 359 356 355 353 345
CAN WHO MY SO NO WHEN OUT ITS INTO OTHER THEN SOME TWO ONLY TIME WHAT ABOUT COULD FIRST THAN MR YOUR ME THEM NEW ALSO
2,079 1,984 1,949 1,923 1,908 1,902 1,880 1,833 1,653 1,586 1,557 1,548 1,540 1,532 1,529 1,516 1,511 1,494 1,478 1,429 1,424 1,417 1,414 1,414 1,399 1,372
WHEN BEEN OUT THEM DO MY MORE WHO ME LIKE VERY CAN HAS HIM SOME INTO THEN NOW THINK WELL KNOW TIME COULD PEOPLE ITS OTHER
13,501 13,417 13,361 13,322 12,943 12,761 12,718 12,708 11,697 11,564 11,483 11,271 11,241 11,110 10,537 10,414 10,265 10,246 10,007 9,654 9,549 9,481 9,214 9,083 9,061 8,904
Appendices 151
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
WHO INFORMATION UST TWO PEOPLE DIFFERENT SURVEY PROVIDED STAFF RESULT UNIVERSITY NEED BUT YEAR CARD BECAUSE INTERVIEW EACH USERS MANY USED HAD PUTONGHUA HAS VERY WHEN
(continued)
384 375 375 371 367 366 361 355 354 353 349 347 345 345 340 339 333 332 332 331 331 330 328 324 322 317
ACTIVITIES BAY REQUIRED HONGOKONG CONTROL THERE OUT MAY USE RECLAMATION USED PROJECT RECEIVERS NEW REQUIREMENTS VISUAL EIA SUCH AIRPORT LAND CAN SIGNIFICANT CONSIDERED DISPOSAL HIGH HOWEVER
339 339 337 330 328 326 322 319 311 309 305 301 301 299 297 295 293 290 283 281 279 278 277 268 266 264
SHOULD OVER OUR AFTER LIKE THESE DO US HIM ANY MAY WELL NOW VERY MOST WHERE PEOPLE BETWEEN BACK YEAR MADE SUCH JUST MANY THOSE WORK
1,366 1,322 1,316 1,275 1,227 1,215 1,203 1,188 1,184 1,164 1,164 1,160 1,151 1,114 1,091 1,079 1,056 1,028 1,015 1,014 984 953 931 898 895 885
ONLY IT’S WILL THAN YES JUST BECAUSE TWO OVER DON’T GET SEE ANY MUCH WAY THESE HOW DOWN EVEN FIRST DID BACK GOT OUR NEW GO
8,889 8,848 8,834 8,315 8,234 8,190 8,128 7,334 7,285 7,253 7,241 7,216 7,029 6,795 6,791 6,791 6,758 6,755 6,609 6,410 6,220 6,201 6,190 6,189 6,127 6,029
152 Corpus-based Analyses of Problem-Solution Pattern
Appendices 153
Appendix 4-2. Keyword list in a PROFCORP report 1_ecI008.kws (keyness) N
Word
Freq. % ECL.txt
Freq.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
SLAUGHTERHOUSE IMPACTS MITIGATION ENVIRONMENTAL MEASURES WASTEWATER BPP NOISE STUDY OPERATIONS WASTES SEIA GUIDELINES PROPOSED EPD WILL RECOMMENDED MANAGEMENT ODOUR EMS WASTE IMPACT BLOOD TREATMENT ENSURE AREA CONSTRUCTION SURROUNDING
21 16 12 18 13 7 7 12 15 11 7 6 8 11 5 29 7 10 5 5 7 9 8 8 9 11 8 6
1 5 0 54 83 0 0 78 216 68 3 0 13 100 0 3,107 22 139 2 2 37 126 81 85 165 370 119 39
1.52 1.15 0.87 1.30 0.94 0.51 0.51 0.87 1.08 0.79 0.51 0.43 0.58 0.79 0.36 2.09 0.51 0.72 0.38 0.38 0.51 0.65 0.58 0.56 0.65 0.79 0.58 0.43
Corpus % Key- P ness
0.02
0.31 0.01
0.01
0.02 0.04 0.01
269.2 188.2 158.4 156.9 95.7 92.4 92.4 87.9 87.6 81.6 80.2 79.2 77.7 73.8 66.0 62.2 60.4 59.0 57.6 57.6 53.9 53.0 52.0 51.3 48.3 46.5 46.2 43.9
0000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001
154 Corpus-based Analyses of Problem-Solution Pattern
Appendix 4-3. Keyword list in a STUCORP report 1 cs0006.kws (keyness) N
Word
Freo.
1_CS.txt
Freq.
Corpus
% Keyness P
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
COMPUTER BARNS COST STUDENTS PRINTERS SOFTWARE COMPUTERS PACKAGES TONER FACILITIES HKUST DIAL-1N STUDENT SERVER HARDWARE PROBLEMS COST UTILIZATION ‘INSUFFICIENT NEED FEASIBILITY SERVERS TRAYS INADEQUATE HELPERS TELEPHONE
53 29 22 35 13 17 16 12 8 16 7 6 10 7 7 12 12 5 6 14 5 5 5 6 4 7
2.84 1.55 1.16 1.87 0.70 0.91 0.66 0.64 0.43 0.86 0.37 0.32 0.54 0.37 0.37 0.64 0.64 0.27 0.32 0.75 0.27 0.27 0.27 0.32 0.21 0.37
281 4 0 326 3 66 48 8 0 132 0 0 52 7 22 244 247 3 15 527 6 6 7 23 1 6
0.03
377.9 341.4 277.4 212.9 148.4 130.4 129.9 124.3 100.8 100.8 88.2 75.6 71.4 68.6 56.2 55.3 55.0 52.4 50.5 48.4 47.9 47.9 46.7 46.1 45.4 43.3
0.03
0.01
0.02 0.02
0.05
0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001 0.000000000001
Appendices 155
Appendix 4-4. Key Technical Vocabulary in PROFCORP A. Environmental Study EIA (28) IEIA (2) DEIA (2) SEIA (2) CEIA IAR EDS EIS EISA
Environmental Impact Assessment Initial Environmental Impact Assessment Detailed Environmental Impact Assessment Supplementary Environmental Impact Assessment Conceptual Environmental Impact Assessment Initial Assessment Report Expanded Development Study Environmental Impact Study Environmental Impact and Safety Assessment
B. Environmental rules and regulations EMA (6) HKPSG (4) AQO (2) ASR (2) LWCS (2) WPCO (2) ANL NCO TMP EMS WQOs EMP SMP
Environmental Monitoring and Audit Hong Kong Planning and Standards Guidelines Air Quality Objectives Area Sensitivity Rating Livestock Waste Control Scheme Water Pollution Control Ordinance Allowable Noise Levels Noise Control Ordinance Turfgrass Management Plan Environmental Management System Water Quality Objectives Environmental Monitoring Plan Sewerage Master Plan
C. Areas/zones SIA (2) MBAs CWA CBAs GIA CDA MCZs FCZs SSSI CMPs
Special Industries Area Marine Borrow Areas Cargo Working Area Container Back-up Areas General Industries Area Comprehensive Development Area Mariculture Zones Fish Culture Zones Site of Special Scientific Interest Contaminated Mud Pits
D. Installations (9) NSR(s) ASR(s) (2) PHIs SSR SBC
Noise Sensitive Receiver(s) Air Sensitive Receivers Potentially Hazardous Installations Secondary Surveillance Radar Sub-Marine Power Cable
156 Corpus-based Analyses of Problem-Solution Pattern
LTPs GRS
Large Thermal Power Station Gas Receiving Station
E. Gases / Metals causing environmental damage TSP (7) RSP (3) LFG (3) CFCs SS
Total Suspended Particulates Respirable Suspended Particulates Landfill Gas Chlorofluorocarbons Suspended Solids
Leachate (6) Hydrogen sulphide Carbon dioxide Chloride Oxides Nitrogen Ozone Halons Methane
ferric copper zinc TBT Tributyltin LPG Liquefied Petroleum Gas methanogenic nitrous Ammonia
F. Mitigation Measures LTF CIF RTFs LRWF STW VFPW MDC BPP PPVL KTIPS GRIPD
Leachate Treatment Facility Centralised Incineration Facility Refuse Transfer Facilities Low-level Radioactive Waste Storage Facility Sewage Treatment Works Village Flood Protection Works Main Drainage Channel (works) By-Product Plant Pillar Point Valley Landfill Kwun Tong Intermediate Pumping Station Green Island Reclamation Public Dump
G. Measurements (8) DBa Ha (3) Ug/m3 (3) Cu Mgl Vph Kg. Km.
Decibel scale hectares (measure of TSPs in air) cubic metres milligram vehicles per hour
H. Technical Vocabulary (misc.) (2) Bund Sousa (2) Cantilever
soil wall built across slope to retain water type of dolphin (white) type of barrier
Penaeid Benthic Phytoplankton Grab-dredged (2) Trailer-dredged Washwater Cofferdam Fung Shui
Appendices 157
type of shrimp living on the floor of the sea microscopic plants which float in the sea Stormwater (5) Groundwater (4)
Appendix 4-5. Key Technical Vocabulary in STUCORP Computer-related Internet (7) Modem(s) (5) Dial-in (3) Dial-up (2) Dot-matrix (3) Server(s) (3)
encryption e-mail Aspen Sybase Supernet SunSparc Visual Basic
PCs (2) MS_Dos ISPs PPP (fast modem pool) University Departments / Service Centres UST (22) HKUST (50) CCST (16) SAO (8) ARR (5) SHO (2) SEPO (2) EMO (2) CS (6) CPEG (2) EEE SAC (2) SU HSS
University of Science and Technology Hong Kong University of Science and Technology Centre for Computing Services and Telecommunications Student Affairs Office Admissions, Registration and Records Office Student Housing Office Safety and Environmental Protection Office Estates Management Office Computer Science Computer Engineering Electrical and Electronic Engineering Self-Access Centre Students’ Union Humanities and Social Science
158 Corpus-based Analyses of Problem-Solution Pattern
Appendix 4-6. List of key-key words in PROFOCORP
N
Word
Of 60
As %
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
IMPACTS CONSTRUCTION NOISE MITIGATION ENVIRONMENTAL QUALITY SITE AREA WILL MEASURES MONITORING EIA PROPOSED WATER HONGOKONG RECOMMENDED MARINE IMPACT BE RECEIVERS ASSESSMENT TRAFFIC DREDGING LEVELS SENSITIVE WORKS ROAD RECLAMATION WASTE DUST VISUAL SEDIMENT THE CONTAMINATED DISPOSAL AIR STUDY EPD EXISTING
50 47 44 43 43 39 34 32 30 30 29 28 28 27 27 27 27 26 26 24 23 23 22 22 22 21 20 20 20 20 15 15 15 14 14 14 14 14 13
83.33 78.33 73.33 71.67 71.67 65.00 56.67 53.33 50.00 50.00 48.33 46.67 46.67 45.00 45.00 45.00 45.00 43.33 43.33 40.00 38.33 38.33 36.67 36.67 36.67 35.00 33.33 33.33 33.33 33.33 25.00 25.00 25.00 23.33 23.33 23.33 23.33 23.33 21.67
Appendices 159
(continued) 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
DESIGN SEWAGE BAY CONTRACTOR PHASE LOCATED TUENOMUN DEVELOPMENT OPERATIONAL POLLUTION EMISSIONS SEDIMENTS LANDFILL OPERATION POTENTIAL ODOUR DURING TREATMENT LANDAU RESIDENTIAL AREAS REQUIREMENTS LANDSCAPE RECOMMENDATION PROJECT ISLAND ADJACENT FACILITIES ACTIVITIES DBA PREDICTED OPERATIONS ECOLOGICAL HARBOUR TSP LEACHABLE ALIGNMENT DETAILED IMPLEMENTATION NSRS DREDGED STANDARDS CONTRACT EMA
12 12 12 11 11 11 10 10 10 10 10 10 10 10 10 9 9 9 9 9 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 6 6 6 6 6 6 6 6 6
20.00 20.03 20.00 18.33 18.33 18.33 16.67 16.67 16.67 16.67 16.67 16.67 16.67 16.67 16.67 15.00 15.00 15.00 15.00 15.00 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 13.33 10.00 10.00 10.03 10.00 10.00 10.00 10.00
160 Corpus-based Analyses of Problem-Solution Pattern
(continued) 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 116 119 120 121 122 123 124 125 126 127
SEWERAGE GAS TSINGOYI EFFLUENT MATERIAL STORMWATER CONTAINER TSEUNGOKWANOO DISCHARGE ROADS DISCHARGES CHANNEL FACILITY ROUTE WASTES OPTIONS AUDIT MINIMISE PLAN PORTAL PORT OXYGEN DEDGERS CONCENTRATIONS BARRIERS WOULD PONDS PLANT BLASTING SCHEME DISCOVERY DRAINAGE RIVER CONTAMINATION TERMINAL JETTY COMPLIANCE RESTORATION GROUNDWATER MANAGEMENT ECOLOGY REDUCE SEAWALL PUMPING
6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4
10.00 10.00 10.00 10.00 10.00 8.33 8.33 6.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 8.33 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67
Appendices 161
(continued) 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
FISH FILL ENSURE FISHERIES SILT SUSPENDED STREAM CONTROL MODELLING INDIRECT METALS HKPSG SPOIL SHOULD SCENARIOS KOWLOON ASSOCIATED APPROXIMATELY PFNGOCHAU VEGETATION YUENOLONG WATERS PHASES ORDINANCE VICTORIA TUNNEL
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67 6.67
162 Corpus-based Analyses of Problem-Solution Pattern
Appendix 4-7. List of key-key words in STUCORP
N
Word
Of 80
As %
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
STUDENTS RESPONDENTS HKUST QUESTIONNAIRE STUDENT UST QUESTIONNAIRES INTERVIEW THE CCST COURSES SURVEY DATA USERS WE COURSE HONGOKONG UNDERGRADUATE COMPUTER FIGURE CAN SEMESTER HALL CAMPUS STAFF USE SYSTEM FACILITIES OPINIONS SPORTS BARNS OBSERVATION UNIVERSITY SERVICE LECTURERS INTERVIEWS PROBLEM SAO FEASIBILITY
69 51 50 33 24 22 19 17 17 16 15 14 14 13 13 12 12 12 11 11 11 10 10 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 0
86.25 63.75 62.50 41.25 30.00 27.50 23.75 21.25 21.25 20.00 18.75 17.50 17.50 16.25 16.25 15.00 15.00 15.00 13.75 13.75 13.75 12.53 12.50 12.50 12.50 12.50 12.50 12.50 12.50 11.25 11.25 11.25 11.25 11.25 11.25 10.00 10.00 10.00 10.00
Appendices 163
(continued) 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
STUDY SITUATION SERVICES BARN PRINTERS RESIDENTS QUOTA LIBRARY COMPUTERS INTERNET PROVIDED REGISTRATION ACADEMIC SECURITY THEIR CS PRINTING WERE DEPARTMENT FEE FEASIBLE USUAGE CARD MATERIALS HELPERS HOURS MACHINES BOOKING OUR TIME FOOD ARR UTILIZATION HOMEWORK THEY SCIENCE USING SCHOOL RESOURCES SHOP INSUFFICIENT RECOMMENDATIONS IS PRINTER
8 8 8 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
10.00 10.00 10.00 8.75 8.75 8.75 8.75 8.75 8.75 8.75 8.75 8.75 8.75 7.50 7.50 7.50 7.50 7.50 7.50 7.50 7.50 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25
164 Corpus-based Analyses of Problem-Solution Pattern
(continued) 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
LANGUAGE ASSISNMENTS TERTIARY QUESTIONS CENTER CENTRE RANDOM COPY SOLUTIONS PHOTOCOPYING LABORATORY PERIOD LEARNING SOLUTION JOB THAT EMAIL PASSWORD PRICE SPORT CANTEEN DEPRTMENTS MONEY HK UG PROBLEMS BELONGINGS UNDERGRADUATES HALLS STOLEN USED MOST NEED PUTONGHUA CARDS
5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
6.25 6.25 6.25 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00
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Author index A Ädel, A. 102 Aston, G. 53
Grabe, W. 14, 15 Granger, S. 97 Green, C. 111, 112
B Baker, M. 37 Baker, P. 13, 18 Barlow, M. 97 Belcher, D. 133 Bernardini, S. 136 Bhatia, V. K. 14, 15, 16, 18 Biber, D. 13, 25 Blommaert, J. 18 Bowker, L. 43 Burnard, L. 53
H Halliday, M. A. K. 17, 20, 39, 54, 70, 87, 127 Harris, J. 6 Hasan, R. 54, 70 Hinkel, E. 109, 127 Hoey, M. 1, 2, 34, 39, 48, 58, 105, 138 Howarth, P. 10 Hunston, S. 15, 34, 55, 88, 137 Hyland, K. 19, 68, 102
C Carter, R. 34, 137 Celce-Murcia, M. 100, 137 Coffin, C. 17, 18 Connor, U. 14 Cowie, A. 10 Crombie, W. 54, 62, 85
I Ivanič, R. 7
D De Beaugrande, R. 133 de Haan, P. 24 Devitt, A. 88 F Fairclough, N. 18 Fang, X. 58 Firth, J. R. 8 Flowerdew, J. 7, 10, 24, Flowerdew, L. 3, 10, 35, 54, 69, 130, 136 Francis, G. 7, 8, 105, 106 Fries, P. 85, 86 G Gavioli, L. 90, 134 Gisborne, N. 107 Gledhill, C, 91, 124 Goodman, A. 43
J Johansson, S. 40 Jordan, R. 5, 6, 7 K Kaplan, W. 14, 15 Kennedy, G. 25, 27, 58 Kifle, N. 132 Knowles, G. 31 L Lee, D. 23 Leech, G. 26 Lin, L. 104, 111 Lorenz, G. 102 Louw, B. 32, 58 M Marco, M. J. L. 54 Martin, J. R. 33, 34, 35 Mauranen, A. 40 McCarthy, M. 2, 5, 134, 137 McEnery, T. 13, 14, 25, 132 Meunier, F. 134, 135 Meyer, C. 25, 26
Milton, J. 97, 102, 136, 138 Mohan, B. 135 Moon, R. 65 Mudraya, O. 40 Mukherjee, J. 134 N Nesselhauf, N. 97, 134 O O’Halloran, K. 17, 18 P Partington, A. 14, 34 Payne, E. 43 Pearson, J. 43 Pravec, R. 97 Proctor, M. 4, 5, 8, 34 R Renouf, A. 29, 31, 71 S Schmidt, H.-J. 7, 104, 107 Scott, M. 8, 39, 40, 105 Seidlhofer, B. 40 Sinclair, J. McH. 10, 24, 25, 31, 32, 55, 65, 134, 137, 138 Stubbs, M. 10, 16, 55, 56, 68 Swales, J. 14, 53, 136 T Tognini-Bonelli, E. 18, 26, 31, 138 Tribble, C. 15, 18, 40 W Widdowson, H. 15, 16, 18, 130, 134, 135, 138 Wilkins, D. 76 Williams, G. 26, 38 Winter, E. 4, 5, 6 Y Yang, H. 38, 42
Subject index A abbreviation 27, 28, 29, 43, 48 see also Latin abbreviations 27–29 aboutness 39 academic writing 54, 97, 127 ACRONYM project 29, 71 adjective 10, 34, 38, 55, 78, 82, 83, 90, 93, 104, 108, 118, 119, 120, 121, 123, 125 anaphora 28, 69, 70 see also anaphoric noun 7, 104, 105, 106, 108 anticipatory ‘It’ 111 A-Noun 34, 70, 105, 106, 129 Applied Science component 53, 68, 72, 80, 81, 90, 94, 98, 103, 106, 111, 113, 122, 131 Appraisal system 33, 34 apprentice writers 49, 50, 51, 57, 98, 131, 132 associated with 68, 69, 74, 94, 129, 130 audience 21, 23 authenticity 15, 130, 134, 135, 136, 138 B Bank of English 17, 46, 90 BNC (British National Corpus) 8, 83, 84, 89, 101, 102, 135 see also Applied Science component 53, 68, 72, 80, 81, 90, 94, 98, 103, 106, 111, 113, 122, 131 core written component 35, 36, 40, 56 C cataphoric 70, 78, 102, 104, 105, 106, 116, 118 causation 60, 61, 62, 63, 64, 71, 73, 84, 94, 99, 105, 107, 110, 113, 126, 129, 130
see also cause-consequence 2, 6, 11 cause-effect 54, 59, 76 cause-reason 58, 59, 60, 64, 66, 69, 100, 107, 113 cause 56, 60, 64, 65, 72, 99, 100, 107, 113, 121, 132 CDA 17, 18, 19 classificatory framework 53, 55, 57, 75, 76, 115 clause relations 1–6, 54 COBUILD 35, 36, 67 coherence 7, 46, 84, 91, 93, 110 cohesion 54, 85, 86 colligation 8, 9, 32, 56, 65–69, 79, 86, 111 collocation 8, 9, 10, 32, 38, 39, 48, 55–60, 65, 69, 88, 103, 107, 120, 121, 124, 135, 138 Concord 32, 38, 46, 47, 49 concordance 11, 14–19, 106, 131, 135, 137 Condition-Consequence 59, 64, 83, 103, conjuncts 5, 54 connector 4, 8, 36, 105 connotation 32, 34, 35, 43, 49 context 5, 7, 15, 16, 18, 34, 36, 37, 101 see also contextual features 7, 10, 15, 16, 18, 21, 68, 80, 95 121, 130–134 corpus 8, 12, 14, 15, 20–30, 38, 97, 98, 102, 104, 105, 109, 112, 113, 127, 128, 130–135 see also Bank of English 17, 46, 90 BNC (British National Corpus) 8, 83, 84, 89, 101, 102, 135 corpus compilation 24 corpus, general 8, 22, 25, 35, 36, 39, 40
corpus, learner 97, 127, 128, 132, 134 corpus, reference 8, 10, 22, 40, 53 corpus, specialised 8, 11, 24, 25, 26, 32, 53, 55, 90, 130 International Corpus of Learner English (ICLE) 97 Professional corpus (PROFCORP) 8, 21, 50 Student corpus (STUCORP) 22, 49 critical discourse analysis 17, 18, 19 D data-driven learning (DDL) 134–137 deictic 7, 8, 104 delexical verbs 79, 80 delicacy 25, 55, 74, 78 determiner 69, 105, 107, 119 E English as a lingua franca 40 epistemic 56, 102 ergative verbs 100, 132, 137 error, type of 97, 100, 132 ethnographic considerations 19, 130, 131, 134 evaluation 2, 33–38, 56, 61, 69, 71, 76, 78, 80, 81, 92, 94, 94, 95, 103–106, 110, 116, 118, 120, 121, 126, 127, 135 evoking items 34, 37, 48, 93, 126 existential ‘there’ 59, 60, 66, 74, 101, 132 F formulaic 15, 72, 74, 103, 113, 125
178 Corpus-based Analyses of Problem-Solution Pattern
frequency 8, 10, 15, 25, 33–42, 49, 51, 63, 72, 84, 89, 92, 123
lexis 6, 7, 35, 39, 40, 42, 43, 46, 47, 48, 49, 86
G general corpus 25, 35, 36, 39, 40 genre 10, 13–20, 39, 40, 44, 130 Grounds-Conclusion 54, 57, 62, 64, 70, 84, 85, 89, 91, 98, 103, 111, 121, 122, 124, 125
M Means-Result 59, 64, 66, 70, 98, 103, 108 metadiscourse 7, 102 metalanguage 50, 101, 128, 132 minimise 55, 58, 60, 65, 66, 69, 70, 73, 77, 83, 94, 101, 109 modals 56, 58, 60, 86, 101, 102, 109 multilayering 3, 9, 48, 99
H hedging 19, 68, 76, 94, 130 however 3, 8, 9, 10, 11, 19 hyphenation 29, 30 hyponym 29, 35, 71, 130 I ICE 40 ICLE 97 ICE-HK 40 idiom 65 inductive approach 134–138 inscribed items 34, 81 interlanguage 97, 111, 132 interpersonal 34, 56, 76, 80, 109, 113, 116, 122, 126, 128, 131 interpretation 7, 10, 14–20, 35, 37, 130, 136, 137, 138 intertextuality 79, 80, 88, 89, 92, 95, 123, 130, 131 K key 37, 39, 40–50, 55, 73, 77, 98, 112, 115, 117, 126, 130, 131 keyness 10, 39, 40, 44, 71 key-key word 33, 44, 45, 47, 49, 50, 57, 63, 98, 110, 115 key word 10, 29, 33, 41, 50, 58, 66, 71, 75, 77, 83, 86, 91, 93, 94, 95, 113, 115, 131 L Latin abbreviations 27–29 learner corpus 97, 127, 128, 132, 134 lemma 31, 32, 56, 60, 71, 72, 120 lemmatization 31, 32 lexico-grammatical patterning 14, 54, 55, 57, 64, 72, 73, 77, 78, 80, 92, 93, 99, 105, 106, 109, 118, 122, 124, 126, 131, 134
N native speaker 19, 22, 43, 97, 100, 124 non-native speaker 21, 22, 109, 113 see also NNS 97 nominal 54, 61, 62, 68, 69, 75, 76, 77, 80, 87, 92, 95, 106, 110, 115 nominalisation 17, 88, 122, 123 notional 54, 76, 130 nouns 36, 38, 90, 95 see also A-nouns 34, 70, 105, 106, 129, 130, 132 grammatical metaphor nouns 94, 95, 122, 124 multi-word nouns 27 shell nouns 7, 105, 107 signaling nouns 7, 105 O open class vs. closed class 67 overpassivise 100, 137 P passive 18, 65, 70, 77, 78, 79, 80, 82, 84, 86, 87, 88, 89, 90, 91, 93, 95, 99, 100, 116, 117, 121, 122, 124, 125, 128, 131, 133, 137 pedagogy 129, 133–138 phraseology 8, 36, 55, 56,75, 133, 138 pollution 10, 11, 19, 35, 46, 55 polysemy 65 postmodification 61, 86, 88, 89, 101, 125
premodification 60, 61, 64, 69, 71, 81, 83, 90, 93, 104, 105, 106, 109, 120, 121, 123, 125 preposition 58, 64, 61, 67, 69, 70, 86, 93, 94, 95, 99, 101, 107, 125, 129, 133 prosody 16, 32, 34, 37, 43, 55, 58, 68, 94, 99, 100, 135 R Reason-Result 57, 58, 60, 64, 69, 70, 72, 73, 84, 94, 95, 98, 99, 107, 109, 111, 129 reduce 11, 45, 60, 65, 66, 69, 70, 73, 77, 83, 94, 101 reference corpus 8, 10, 22, 40, 53 register 7, 101, 128, 131, 132 repetition 18, 40, 63, 118 representativeness 21, 24, 25, 26, 32, 33 retrospective 7, 104, 108 Rheme 8, 57, 58, 61, 66, 67, 69, 70, 76, 79, 85, 86, 88, 101, 102, 119, 120, 124, 139 rhetorical 13, 15, 51, 86, 89, 103, 129, 130 see also New Rhetoric 19, 88 S scheme 46, 70 semantic prosody 16, 34, 37, 48, 55, 58, 94, 99, 100, 130, 134 semantic relations 4, 53, 54, 76, 107 sentence boundary 102, 105 shell nouns 7, 105, 107 signaling nouns 7, 105 size of corpus 21, 23, 24, 25, 26, 30, 32, 40 specialised corpus 8, 11, 24, 25, 26, 32, 53, 55, 90, 130 style 26, 30, 40, 84, 125 superordinate 34, 35, 41, 48, 50, 71, 75, 90, 93 synonym 2, 6, 32, 46, 65, 71, 90, 108, 111, 130 systemic-functional grammar 17, 20, 33, 130
T thematisation 79, 94, 119 Theme 8, 53, 57, 58, 61, 66, 67, 69, 70, 76, 79, 85, 86, 88, 91, 102, 119, 120, 124 token 27, 40 type 27 U unhedged 109 unidiomatic 107, 132
Subject index 179
V verbs, ergative 100, 132, 137 verbs, delexical 79, 80 verbs, two-way signalling 70, 73, 132, 136 vocabulary 1 & 2 items 4, 5 vocabulary 3 items 6, 7, 34, 36, 37 vocabulary, sub-technical 37– 40, 42, 44, 48, 49, 50, 64, 65
vocabulary, technical 38, 39, 42, 43, 45, 61, 63 W word boundaries 27 wordlist 27, 28, 35, 36, 42, 44 WordSmith 30, 38, 46 writers, apprentice 49, 50, 51, 57, 98, 131, 132
In the series Studies in Corpus Linguistics (SCL) the following titles have been published thus far or are scheduled for publication: 30 Adolphs, Svenja: Corpus and Context. Investigating pragmatic functions in spoken discourse. ix, 152 pp. + index. Expected February 2008 29 Flowerdew, Lynne: Corpus-based Analyses of the Problem–Solution Pattern. A phraseological approach. 2008. xi, 179 pp. 28 Biber, Douglas, Ulla Connor and Thomas A. Upton: Discourse on the Move. Using corpus analysis to describe discourse structure. 2007. xii, 290 pp. 27 Schneider, Stefan: Reduced Parenthetical Clauses as Mitigators. A corpus study of spoken French, Italian and Spanish. 2007. xiv, 237 pp. 26 Johansson, Stig: Seeing through Multilingual Corpora. On the use of corpora in contrastive studies. 2007. xxii, 355 pp. 25 Sinclair, John McH. and Anna Mauranen: Linear Unit Grammar. Integrating speech and writing. 2006. xxii, 185 pp. 24 Ädel, Annelie: Metadiscourse in L1 and L2 English. 2006. x, 243 pp. 23 Biber, Douglas: University Language. A corpus-based study of spoken and written registers. 2006. viii, 261 pp. 22 Scott, Mike and Christopher Tribble: Textual Patterns. Key words and corpus analysis in language education. 2006. x, 203 pp. 21 Gavioli, Laura: Exploring Corpora for ESP Learning. 2005. xi, 176 pp. 20 Mahlberg, Michaela: English General Nouns. A corpus theoretical approach. 2005. x, 206 pp. 19 Tognini-Bonelli, Elena and Gabriella Del Lungo Camiciotti (eds.): Strategies in Academic Discourse. 2005. xii, 212 pp. 18 Römer, Ute: Progressives, Patterns, Pedagogy. A corpus-driven approach to English progressive forms, functions, contexts and didactics. 2005. xiv + 328 pp. 17 Aston, Guy, Silvia Bernardini and Dominic Stewart (eds.): Corpora and Language Learners. 2004. vi, 312 pp. 16 Connor, Ulla and Thomas A. Upton (eds.): Discourse in the Professions. Perspectives from corpus linguistics. 2004. vi, 334 pp. 15 Cresti, Emanuela and Massimo Moneglia (eds.): C-ORAL-ROM. Integrated Reference Corpora for Spoken Romance Languages. 2005. xviii, 304 pp. (incl. DVD). 14 Nesselhauf, Nadja: Collocations in a Learner Corpus. 2005. xii, 332 pp. 13 Lindquist, Hans and Christian Mair (eds.): Corpus Approaches to Grammaticalization in English. 2004. xiv, 265 pp. 12 Sinclair, John McH. (ed.): How to Use Corpora in Language Teaching. 2004. viii, 308 pp. 11 Barnbrook, Geoff: Defining Language. A local grammar of definition sentences. 2002. xvi, 281 pp. 10 Aijmer, Karin: English Discourse Particles. Evidence from a corpus. 2002. xvi, 299 pp. 9 Reppen, Randi, Susan M. Fitzmaurice and Douglas Biber (eds.): Using Corpora to Explore Linguistic Variation. 2002. xii, 275 pp. 8 Stenström, Anna-Brita, Gisle Andersen and Ingrid Kristine Hasund: Trends in Teenage Talk. Corpus compilation, analysis and findings. 2002. xii, 229 pp. 7 Altenberg, Bengt and Sylviane Granger (eds.): Lexis in Contrast. Corpus-based approaches. 2002. x, 339 pp. 6 Tognini-Bonelli, Elena: Corpus Linguistics at Work. 2001. xii, 224 pp. 5 Ghadessy, Mohsen, Alex Henry and Robert L. Roseberry (eds.): Small Corpus Studies and ELT. Theory and practice. 2001. xxiv, 420 pp. 4 Hunston, Susan and Gill Francis: Pattern Grammar. A corpus-driven approach to the lexical grammar of English. 2000. xiv, 288 pp. 3 Botley, Simon Philip and Tony McEnery (eds.): Corpus-based and Computational Approaches to Discourse Anaphora. 2000. vi, 258 pp. 2 Partington, Alan: Patterns and Meanings. Using corpora for English language research and teaching. 1998. x, 158 pp. 1 Pearson, Jennifer: Terms in Context. 1998. xii, 246 pp.