A Sociolinguistic Study of the Realization of Refusals Among Yemeni EFL Learners
Bibliographic record
Abstract
The present paper attempts to study the realization of refusal responses to invitations and requests among Yemen EFL learners in equal, higher and lower social status. It also aims to find out the pragmatic failure resulted from negative pragmatic transfer. In order to do so, refusals of 40 Yemeni EFL (20 high and 20 low proficient) learners were compared with refusals of 20 native speakers of English (ENS) and 20 native speakers of Arabic (ANS). Data were collected using a Written Discourse Completion Test (WDCT) consisting of six refusals to invitations and requests in higher, equal and lower social status. This study finds out that Yemenis and Americans used different refusal strategies when refusing persons of equal and lower social status. ANS also used the adjunct of invoking the name of God which is religiously rooted and culturally specific to assert their excuses. Interestingly, Yemeni EFL learners showed a tendency toward the L1 pragmatic norms in the use of invoking the name of God and also in the use of more Direct strategies when refusing someone equal or lower in status. With respect to the content of refusals, Yemenis used general and vague excuses when refusing someone equal or lower in social status whereas Americans, on the other hand, were found to use detailed and clear excuses with persons of different social status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".