Bibliographic record
Abstract
Although a number of interlanguage studies on apologies have been conducted, there has been no study of apologies among Thai EFL learners that differentiates between learners with high and low levels of exposure to the target language. This study, differentiating between learners with high and low levels of exposure to the target language, addresses two research questions: (i) What are the similarities and differences between perception of offense context of Thai EFL learners and native speakers of English? (ii) What are the similarities and differences between apology strategies used by Thai EFL learners and native speakers of English? Data for the analysis came from a questionnaire consisting of 12 scenarios eliciting different offense contexts. The participants were divided into four groups: native speakers of English (NEs), native speakers of Thai (NTs), Thai EFL learners with high exposure to the target language (TEHs) and Thai EFL learners with low exposure to the target language (TELs). The results revealed that due to the influence of cultural background, the learners’ perception of offense context was dissimilar to the NEs in certain respects. However, the investigation of apology strategies showed that compared to the TELs, the TEHs’ apology strategies tended to be less influenced by their cultural background and more similar to the NEs’. Providing support for levels of exposure to the target language, as individual differences vary in L2 pragmatic development, the study sheds light on pedagogical intervention that may enhance learners’ pragmatic competence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".