EFL Undergraduate Learners’ Politeness Strategies in the Speech Act of Disagreement
Bibliographic record
Abstract
This study investigated politeness strategies of disagreement expressed by undergraduate Iraqi EFL students in Online Asynchronous Discussions OADs. The data were collected from 100 individuals randomly chosen from undergraduate classes at Mosul university. The investigation focused on the impact that gender may have on politeness strategies that could be used to lessen the possibility of conflict in expressing the face-threatening act FTAs. For data collection, the participants were required to fill a Discourse Completion Test (DCT), adapted from Rasekh and Simin (2015) to simulate online asynchronous discussions. Muntigl and Turnbull's (1998) taxonomy was used to identify disagreement expressions. For politeness investigation, Brown and Levinson’s (1987) theory was adopted. The study demonstrated that both males and females do not consider others’ faces. In addition, it showed no regard for interlocutors' power and social statuses in expressing the FTAs. However, the results provided a valuable insight for teachers and curriculum designers, generally in EFL and the Iraqi context in specific. Pedagogical recommendations are discussed based on the findings.
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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 | 0.003 | 0.012 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".