Abstract
This paper aims to investigate Asian English learners’ preferred uses of the modal auxiliaries must and should by using both syntactic, topical and semantic annotation as well as various corpus linguistic tools. Our primary goal is to identify culture-specific motivations in the use of the two modal verbs and to demonstrate how those relate to the use of the two items in language produced by L1 speakers of English. We adhere to Hinkel’s study (TESOL Q 29(2):325–343. 1995, doi: 10.2307/3587627) and her findings as a springboard into our analysis. She suggested that differences in the use of modals between Asian English learners (AELs) and English L1 speakers may be culturally founded. We see the essential need to question and deepen the understanding of such implications, arguing that a count of tokens and their correlation with essay topics alone cannot be sufficiently informative. This is why we pursue a more in-depth approach which combines both corpus and pragmatic tools. First of all, we will conduct a contrastive analysis of quantitative data from two different learners’ corpora on the one hand, and three available corpora of contemporary English on the other hand. The goal of this analysis is to carve out certain AEL and/or English native speakers’ patterns in using must and should in their deontic and epistemic senses. Secondly, we will evaluate the AEL data in regard to whether and how they reflect cultural values different from the ones expressed in native English speech. Our findings show that, whereas ENSs and AELs alike do demonstrate a preference in the less face-threatening modal should over the more direct must in deontic uses, both must and should tend to be used more deliberately and more purposefully by AELs than by ENSs, who seem to apply them much more vaguely and polysemously. With regard to Hinkel’s major claim that AELs’ use of must and should reflects cultural values different from the ones expressed in native English speech, we were able to affirm this hypothesis. AELs in our datasets use must and should significantly differently than ENSs due to their alleged culturally intrinsic sense of togetherness and joint responsibility for their society.
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Notes
In this study we exclude the negative forms mustn’t, shouldn’t.
Dutra adds ‘advisability’ to the dynamic framework of modality, which “is another root meaning not as close to obligation and necessity as [necessity and obligation] may be to each other” (1998: 22).
Note that we only consider relevant what the OED refers to as the lexeme must, v.1 and should only in its entries below shall, v., senses 18., 19. and 21. We excluded all OED entries that were either marked <Obs.> or <†>, i.e. as obsolete or for which the OED did not provide any quotations dating after 1990.
Also cf. Viana (2006) on the same observation for Brazilian students.
The sentences were obtained from the Cambridge Learner Corpus First Certificate in English (FCE) dataset, which contains 1244 exam scripts written by candidates who sat the Cambridge ESOL First Certificate in English examination in either 2000 or 2001 (cf. Berzak 2016: 738; Yannakoudakis et al. 2011).
All in all, ICNALE contains 1.8-million-words of controlled L2 English speeches and essays by over 3500 college undergraduate and graduate students in ten countries and areas in Asia as well as L1 productions by 350 English native speakers.
Brown was compiled by W. Nelson Francis and Henry Kučera at Brown University and contains more than 1 million words of running text of edited English prose printed in the United States during the calendar year 1961.
A crosscheck with ICNALE via #LancsBox has revealed that out of 237 <should have> patterns in the AEL ICNALE dataset only 6 instances are <should + have + past participle> constructions.
Note that the AntConc token counts also include those tokens that we manually excluded during the syntactic and semantic annotation. Since the discrepancies are minor and also since we are going to discuss random samples individually, we do not expect them to affect the integrity of our argumentation.
Log likelihood considers the number of words in the focus corpus in relation to a reference corpus (here: Brown), and uses the following formula: G2[LL] = 2*((a*ln (a/E1)) + (b*ln (b/E2))), with the variables a and b representing the number of tokens in either corpus.
COCA provides a list of the 5000 most frequent words in the corpus on: http://www.wordfrequency.info/free.asp?s=y. Accessed 20 Nov 2016.
45,558 hits for have to + 21,752 hits for had to + another 8224 hits for has to.
I.e. <modal auxiliary + I/we + lexical verb…?> and <how/whether/what/which etc. I/we + modal auxiliary + lexical verb>.
Random checks revealed that, if we had considered the collocates of e.g. a span of 5 words left and 5 words right (e.g. we—should with a span of 5< >5), we would have seen numbers about a third higher than the ones we received from the immediate collocates (e.g. 6530 collocates of we—should with a span of 5< >5).
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Kecskes, I., Kirner-Ludwig, M. “It Would Never Happen in My Country I Must Say”: A Corpus-Pragmatic Study on Asian English Learners’ Preferred Uses of Must and Should . Corpus Pragmatics 1, 91–134 (2017). https://doi.org/10.1007/s41701-017-0007-x
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DOI: https://doi.org/10.1007/s41701-017-0007-x