A Comparative Study on the Use of Compliment Response Strategies by Persian and English Native Speakers
Bibliographic record
Abstract
The significance of pragmatic knowledge and politeness strategies has recently been emphasized in language learning and teaching. Most communication failures originate in the lack of pragmatic awareness which is evident among EFL learners while communicating with English native speakers. The present study aimed at investigating compliment response strategies, as a sub-category of politeness strategies, used by a group of Persian and English native speakers, and examining the effect of gender on the use of strategies to respond to compliments. To these ends and with the use of convenient sampling, thirty Iranian native speakers (15 females and 15 males) in Iran and 26 English native speakers (13 females and 13 males) in Canada, all college students with age range of 17-30, participated in this study. In order to collect a corpus of compliment responses, a researcher-made questionnaire in the form of a Discourse Completion Task was distributed among the participants. Using two-way ANOVAs, the findings indicated that there is a significant difference between Persian native speakers and Canadian English speakers ( p< . 05) with respect to the compliment response strategies investigated in this study namely, accept, evade , and reject . Moreover, it was found that the most widely used compliment response strategy among both Iranian and English participants is accept. Regarding the effect of gender on the use of compliment response strategies by the participants, the results did not reveal a statistically significant difference between the two groups ( p > .05).Considering the findings of the present study, materials developers and textbook writers can make more space in EFL textbooks for exercises about compliments (responses); and help in highlighting the significance of this aspect of pragmatic knowledge. Keywords: pragmatics, politeness, compliment, compliment response, EFL
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".