Bibliographic record
Abstract
This article presents the results of an analysis of apology strategies in native and non-native French in Canadian context. The data used were obtained through a Discourse Completion Task questionnaire that was completed by a group of native French speakers (FL1) and a group of learners of French as a second language (FL2). The goal was to identify and compare pragmatic and linguistic choices made by both groups when apologizing in three different situations. Several differences and similarities emerged between the two groups regarding the use of exclamations to introduce apologies, direct apologies, indirect apologies, and supportive acts. For instance, it was found that the FL1 speakers used “expressions of regret”, “offers of apology” 15 and “requests for forgiveness” to apologize directly, while the FL2 speaking informants used 16 only “expressions of regret” and “offers of apology”. While the respondents of both groups 17 mostly chose “offers of repair” to apologize indirectly, they displayed divergent preferences 18 regarding the use of other indirect apology strategies. Differences were also documented 19 with respect to the use of intensification devices in direct apologies and the use of supportive acts. Implications of the findings for L2 French pedagogy were also discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".