An Exploration of Upper-intermediate Iranian EFL learners’ Perception of Politeness Strategies and Power Relation in Disagreement
Bibliographic record
Abstract
The present study was designed to examine the ways power relations influence politeness strategies in disagreement. The study was an attempt to find out whether different power status of people influencethe the choice of appropriate politeness strategies and speech act of disagreement by Iranian EFL learners, in a university setting. A Discourse Completion Test (DCT) was utilized to elicit the required data. The sample included 20 Iranian upper-intermediate EFL learners who were selected based on their scores on a proficiency test. The DTC consists of five scenarios in which the subjects are expected to disagree with two higher statuses and two with peers and one with a lower status. Selection of disagreement situations in DCT was based on relative power and status of people. The main frameworks used for analyzing data were the taxonomy from Muntigl and Turnbull (1995) for counting and analyzing the utterances of disagreement and Brown and Levinson’ (1987) theory of politeness. It was found that EFL learners employ different kind of politeness strategies in performing this face threatening speech act. When performing the speech act of disagreement, they used more direct and bald on record strategies. The findings of this study provide some evidences for the relation between the type and frequency of disagreement and choice of politeness strategies associated with people with different power status. It concludes by arguing that the results can be closely related with learning contexts and textbook contents and some suggestions were put forward regarding the issue.It is also hoped that the findings of this study will provide some worthwhile knowledge into the teaching and training of communication skills in EFL courses. Furthermore, this study may reveal some cultural differences between Iranian societies and others.
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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 | 0.007 |
| 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 | 0.001 |
| 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".