A Cross-Cultural Study of Offering Advice Speech Acts by Iranian EFL Learners and English Native Speakers: Pragmatic Transfer in Focus
Bibliographic record
Abstract
The purpose of the present study was to compare the speech act of offering advice as realized by Iranian EFL learners and English native speakers. The study, more specifically, attempted to find out whether there was any pragmatic transfer from Persian (L1) among Iranian EFL learners while offering advice in English. It also examined whether Iranian EFL learners’ perception of directness/indirectness in the realization of offering advice develops as a result of proficiency development. In order to achieve the objectives, a Discourse Completion Test (DCT) was used to collect the speech act of offering advice from among Iranian EFL learners and native English speakers. The findings indicated that Iranian EFL learners were not as balanced as native English speakers in the use of indirect use of offering advice. It was also observed that Iranian EFL learners had not acquired the pragmatic competence to offer native-like advice with regard to social power and social distance between interlocutors. The result also revealed that Iranian EFL learners and the native English speakers favored a number of similar strategies for the realization of offering advice, though there were differences in terms of frequency use of the speech acts of offering advice. Thus, this study showed evidence of pragmatics transfer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".