The Realization of Complaint Strategies among Iranian Female EFL Learners and Female Native English Speakers: A Politeness Perspective
Bibliographic record
Abstract
Speech acts are interesting areas of research and there has been much research on speech acts. Complaint is a type of speech act and how to use it in interaction is important to EFL learners. The complaint strategies employed by Iranian female EFL learners and female English native speakers were compared in this study. Also, the effects of contextual variables (Social distance and Social power) on the choice of complaint strategies by Iranian female EFL learners and female native English speakers were studied in this research. Thirty Iranian female EFL learners and thirty female native English speakers participated in this study. The two instruments which were used in this study included Oxford Placement Test (OPT) and Discourse Completion Test (DCT). The (DCT), as an open-ended questionnaire was administrated to them to elicit complaint speech acts. Then, the collected data were analyzed according to a modified taxonomy of complaint strategies proposed by Trosoborg (1995). The results indicated that there was a significant difference between Iranian female EFL learners and female native English speakers in terms of using complaint strategies. Iranian female EFL learners used indirect complaint, while female native English speakers used the direct complaint more frequently; and contextual variables had a great influence on complaint strategy choice by participants of two groups.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".