Difficulté des listes thématiques d'un ouvrage bilingue selon la fréquence d’usage des mots
Bibliographic record
Abstract
Résumé : Les ouvrages de vocabulaires thématiques (OVT) sont formés de listes de mots dont le choix est souvent subjectif. Pourtant, la fréquence d’usage des mots permet de sélectionner le vocabulaire le plus courant qui est normalement appris en priorité. Dans la présente recherche, nous analysons un OVT, destiné aux apprenants de niveaux B1-B2 selon l’échelle du Cadre européen commun de référence pour les langues (CECRL), afin de vérifier la difficulté des listes thématiques de mots à l’aide de leur fréquence d’usage. Les résultats obtenus démontrent que la fréquence n’est aucunement prise en compte dans cet ouvrage et que le vocabulaire semble être choisi de manière subjective. Nous faisons alors plusieurs suggestions pour améliorer la réalisation des OVT. Abstract : Thematic Vocabulary Books consist in being lists of words whose choice is often subjective. However, the frequency of use of words makes it possible to select the most common vocabulary which is normally learned in priority. In this research, we analyze an Thematic Vocabulary Book, intended for B1-B2 level learners according to the Cadre européen commun de référence pour les langues (CECRL) scale, to check the difficulty of thematic lists of words with the help of their frequency of use. The results obtained show that the frequency is not taken into account in this book and that the vocabulary seems to be chosen subjectively. We then make several suggestions to improve the construction of Thematic Vocabulary Books.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".