Apology Strategies in the Target Language (English) of Israeli-Arab EFL College Students Towards Their Lecturers of English Who are also Native Speakers of Arabic
Bibliographic record
Abstract
This paper investigates the apology strategies used by Israeli Arab EFL college students in the target language, English toward their lecturers of English who are also Arab native speakers. Analysis of the apology strategies were based on strategies developed by a number of researchers (Owen, 1983; Blum-Kulka & Olshtain, 1984; Trasborg, 1987; Hussein & Hamouri, 1998). It is based on 42 apology e-mails sent by the students to three Arab lectures of English in the college. These e-mails were written in English. 240 apology utterances were performed in these messages. Frequencies and percentages are considered. The findings of the study reveal that the main apology strategy used by the students is “expression of apology”. This strategy consists of three sub-strategies: Expression of regret, offer of apology and request for forgiveness. The next frequent apology strategy used is “Acknowledgement of responsibility” which includes five sub-categories: explicit acknowledgement, expression of lack of intent, expression of self-deficiency, expression of embarrassment and explicit acceptance of the blame. Other strategies such as expression of concern for the hearer, offer of repair, explanation of account and others were also used, but in low frequencies. Key words: Speech acts; Apology strategies; Israeli Arab EFL students; Target language (TL)
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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 | 0.008 |
| 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 | 0.000 | 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".