World Englishes: How Differently Canadian English Native Speakers and Iranian EFL Learners Make Yes/no Question Variants
Bibliographic record
Abstract
This paper investigates the commonalities among linguistic structures despite differences in different varieties of English. It, further, probes the proximity of yes/no question variants produced by Canadian English native speakers and those produced by Iranian intermediate EFL learners. The functions of such question variants are also probed in this study. Making use of an Edinburgh Map Task, 60 Canadians and Iranians performed the task and made English yes/no question variants considering the context and functions of the questions. Based on the results, both groups utilized the same type of yes/no question variants with the same functions. However, with respect to quantity, Canadians made more variants while the context was similar. Another difference noticed was the most frequent variant: Iranians’ frequent variant coincided with the informal context, the Canadians’, yet, did not. These findings revealed that both Canadians and Iranians from two different circles syntactically and pragmatically behaved similarly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".