Bibliographic record
Abstract
The present study examines differences and similarities in the realization of compliments (on skills) in Cameroon and Canadian French. The data were collected by means of discourse completion tasks (DCT) administered to 55 participants in Yaoundé (Cameroon) and 39 respondents in Montréal (Canada). The 277 compliments obtained were analyzed according to the following three aspects: a) head act strategies (direct and indirect compliments), b) lexico-semantic and syntactic features of complimentary utterances, and c) external modification. With regard to head act strategies, the results show a preference for double head acts by the Cameroonian participants, while the Canadians more frequently employed single head acts. It was also found that indirect realizations of head acts occurred only in the Cameroonian data. Positive evaluation markers (e.g. adjectives, adverbs, verbs) and syntactic devices appearing in the compliments varied in type and frequency in the two varieties of French under investigation. The analysis of external modifications reveals that participants of both groups used many speech acts to externally modify their compliments. Overall, interjections, address forms, greetings, self-introductions and apologies were used as pre-compliments, with some speech acts, namely greetings and self-introductions, occurring only in the Cameroonian data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".