Gender-oriented Commonalities among Canadian and Iranian Englishes: An Analysis of Yes/No Question Variants
Bibliographic record
Abstract
This study investigatesvariability in English yes/no questions as well as the commonalities among yes/no question variants produced by members of two different varieties of English: Canadian English native speakers and Iranian EFL learners.Further, it probes the role of gender in theEnglish yes/no question variants produced by Canadian English native speakers and those produced by Iranian EFL learners. A modified version of the Edinburgh Map Task was used in data collection. 60 Canadians and Iranians performed the task and made English yes/no question variants considering the informal context. Based on the results, the same types of yes/no question variants were produced by both groups. However, with respect to quantity, Canadians made more variants while the context of use was similar. Another difference noticed was the most frequent variant: Iranians’ frequent variant coincided with the informal context, yet the Canadians’ frequent variant did not. Regarding gender, Iranians did not produce any gender-based variant; while Canadians showed that their production of yes/no question variants was gender-oriented. These findings revealed that both Canadians and Iranians from two different varieties of English syntactically behaved similarly, but their sociolinguistic behavior was not the same.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".