Enseigner des expressions figées métaphoriques françaises avec l’approche de traduction/comparaison à des apprenants vietnamiens
Bibliographic record
Abstract
Résumé Afin de mettre en évidence la pertinence de l’enseignement/apprentissage des expressions figées métaphoriques (EFM) avec une approche de traduction/comparaison, une intervention se composant d’un cours théorique et de quatre cours pratiques portant sur un corpus de 94 EFM a été mise en place auprès de 69 étudiants locuteurs du vietnamien de niveau avancé en français langue étrangère. L’effet de cette intervention sur la rétention, la compréhension et la production (traduction) des EFM a été évalué au moyen de trois épreuves. Les résultats positifs obtenus par les étudiants à ces dernières ont permis aux auteures de recommander l’approche de traduction/comparaison dans l’enseignement des EFM du français langue étrangère et la poursuite des recherches dans d’autres contextes (autres langues, autres apprenants). Abstract In this study, the authors highlight the relevance of the use of the translation/comparison approach in the teaching and learning of fixed metaphorical expressions (FME) in French as a foreign language. To test the translation/comparison approach, an intervention consisting of one theoretical course and four courses built on 94 FME was applied. Sixty-nine advanced French foreign language Vietnamese students were the subjects of this intervention. The effect of the intervention on the retention, comprehension, and production (translation) of the FME studied, was assessed using three tests. The results obtained tend to demonstrate the positive effect of the translation/comparison approach on the measured variables. It is therefore recommended that the translation/comparison approach be used in the teaching of French foreign language FME and that research be continued in other contexts (other languages, other students).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".