Modals and Quasi-modals of Obligation and Necessity in Indian and Canadian English.
Bibliographic record
Abstract
This study contributes to the existing research regarding the frequency and distribution of modals and quasi-modals in varieties of English. The majority of the authors agree that, during the second half of the 20th century, there has been a general decline in the frequency of core modal auxiliaries of English (Leech, 2013). As a consequence, this has meant an increase in use of semi-modals (Collins, 2009). It has been hypothesized that some of the possible reasons which may explain this new trend have been a great acceptance among speakers of processes of grammaticalization, colloquialization (Leech, 2012) and democratization (Smith, 2012). This interpretation, although widely accepted, has also been questioned by dissenting voices (Millar, 2009). Most of the literature has discussed extensively the differences in relation to this topic between Standard English (SE) and American English (AmE), but there is not such a comprehensive literature concerning other cross- varietal differences of the Outer Circle (Loureiro-Porto, 2019). Modality encompasses a wide range of different semantic notions. This paper explores the differences of the distribution and use of the modals and semi-modals which convey both deontic and epistemic obligation and necessity in Canadian and Indian English. More precisely, must and need are compared to its counterparts and its semantically related quasi-modals have got to and need to. Following Kachru’s model of World Englishes (1992), Canadian English (CanE) has been selected as the representative of an Inner Circle variety, whereas Indian English (IE) has been chosen as representative of an Outer Circle variety. The aim of this paper is to provide new insight into the patterns of distribution between these pairs of modals and semi-modals and to outline some possible reasons for the existing differences. For this purpose, the online Corpus of Global Web-Based English (GloWbE) has been employed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".