Investigation into Strategies of Requests and Mitigation Used by Palestinian English Language Learners
Bibliographic record
Abstract
This study investigates the strategies of request speech act used by Palestinian EFL learners at the school level and the effect of the gender variable on strategy use. Moreover, strategies used to mitigate requests were investigated. Data was collected for this study through completion of Discourse Completion Tasks (DCT). A DCT was designed, consisting of (8) situations, with different social status levels between the participants. The DCT was completed by a sample of (69) EFL learners at the American School in Beit Jala. The researchers adopted Blum-Kulka’s (1989) method in coding and analyzing the data. The findings revealed that some strategies were employed more often than the other strategies. Furthermore, the learners used few strategies to mitigate requests. The findings revealed gaps in learners’ pragmatic competence that call for remediation through designing extra materials that expose the learners to the different strategies that realize speech acts. Finally the findings showed that there is no statistically significant difference in the employment of the strategies due to gender. Further research should be conducted on other English speech acts besides requests, taking into account the effect of social variables, such as social power and distance, on strategy use.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".