Un dictionnaire de reformulation pour les apprenants du français langue seconde
Bibliographic record
Abstract
Nous présentons une recherche en lexicologie appliquée à l’enseignement du français langue seconde qui vise le développement d’un dictionnaire électronique de type particulier, appelédictionnaire de reformulation. Ce dictionnaire devrait permettre aux étudiants de niveau intermédiaire à ceux de niveau avancé de surmonter des difficultés que présente une utilisation souple et idiomatique du lexique du français dans la production langagière. Les questions abordées sont les suivantes : les fondements théoriques et la méthodologie pour l’élaboration d’un tel dictionnaire, le concept d’erreur lexicale et la description des erreurs lexicales à l’aide d’outils formels que nous proposons, l’architecture du dictionnaire de reformulation et l’implémentation de ce dernier. Le cadre théorique dans lequel se situe notre recherche est celui de la théorie linguistique Sens-Texte.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".