Request Strategies: A Contrastive Study Between Yemeni EFL and Malay ESL Secondary School Students in Malaysia
Bibliographic record
Abstract
The aim of this study was to investigate the types of request strategies employed by Yemeni and Malay secondary school students in English language. It also aimed at investigating the influence of social power and social distance on the students’ choice of request strategies. The data was collected through a discourse completion test (DCT) and the analysis used both Blum-Kulk’s et al. (1989) Cross-Cultural Speech Act Realization Patterns (CCSARP), and Scollon and Scollon’s (1995) politeness system. The findings of the study showed that both groups often use non-conventionally indirect request strategies by means of query preparatory. The analysis revealed that both groups do not take into consideration the social power and the social distance between the interlocutors because they always use the same strategies with any person. The students have this sociopragmatic knowledge in their mother tongue; however, both groups are not sensitive to the social power and social distance existing between the interlocutors as they lack the sociopragmatic knowledge in the target language. Moreover, the students almost use the same strategies even though they have different cultural backgrounds, and this might be attributed to their assimilation in the school learning environment which is a positive indicator for conductive learning environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".