Bibliographic record
Abstract
« Anglais intensif : transfert des savoirs et développement lexical » par Nancy Gagné \nRésumé : \nCette étude se penche sur le développement lexical de jeunes francophones (n = 47, âge moyen : 11) inscrits dans un programme d’anglais intensif au Québec (Canada). La production orale des participants a été évaluée à partir d’une narration basée sur une série d’images (Derwing, Rossiter, Munro, & Thomson, 2004) afin de déterminer dans quelle mesure le programme de formation par compétences basé sur une approche communicative permet le développement lexical. Les résultats montrent que les élèvent améliorent leur diversité lexicale, qu’ils utilisent plus de mots anglais, mais que la densité et la sophistication n’augmentent pas de manière significative entre le début et la fin du programme. \nMots clés : Acquisition d’une langue seconde - anglais intensif - contexte d’apprentissage formel - développement lexical \n \nAbstract : The study investigated the lexical development of French-speaking Grade 6 learners (n = 47 mean age : 11) enrolled in a 10-month intensive English program in Quebec, Canada. Measured at the beginning (Time 1) and after a 9-month study period (Time 2), oral production was assessed by means of a picture-cue narrative task (Derwing, Rossiter, Munro, & Thomson, 2004) to determine to what extent intensive English, based on a communicative approach in a competency-based program promotes lexical development. Findings revealed improvement between T1 and T2 in terms of number of English words, lexical diversity, but no significant improvement was found in terms of lexical sophistication and density. \nKey words : Second language acquisition - intensive English - formal settings - lexical development
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".