A Contrastive Study of the Use of Apology Strategies by Saudi EFL Teachers and British Native Speakers of English: A Pragmatic Approach
Bibliographic record
Abstract
This study examines the apology strategies used by 30 British native speakers of English and compares them with those employed by 30 Saudi EFL teachers, using a Discourse Completion Task (DCT). The study considers expressions of regret based on gender, cultural differences and severity of the offence. It is a quantitative, descriptive research study; it relies in its data collection process on a DCT whose reliability and internal and external validity are verified. It investigates three categories of variables types: binary, nominal and ordinal. The binary variables refer to gender, i.e., male and female, the nominal category is concerned with Arabic and English languages, and ordinal variables refer to the most frequent apology strategies employed by the respondents. The present study uses a quantitative method of data analysis which employs descriptive statistics (i.e., frequency analysis and percentages) in order to address the research questions and indicate the types of apology strategies that are frequently used by the speakers of the two investigated groups. The findings show different ways of using apology strategies by the two investigated groups based on the variables considered. Finally, the study concludes with some pedagogical implications for EFL teachers in the Kingdom of Saudi Arabia (KSA).
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".