L’approche neurolinguistique : une nouvelle conception du « comment » on apprend une langue seconde
Bibliographic record
Abstract
L’approche neurolinguistique (ANL) est une nouvelle conception de l’apprentissage et de l’enseignement d’une langue seconde (L2). L’approche vise à créer en salle de classe les conditions nécessaires pour que les apprenants puissent apprendre à utiliser la langue aux fins de communication. Ses fondements théoriques sont basés sur les recherches les plus récentes en neurolinguistique (Paradis, 1994, 2004, 2009; N. Ellis, 2011). Les principes pédagogiques de l’ANL ont été conceptualisés par Netten et Germain, inspirés de leur expérience dans le système scolaire et de leurs recherches en salle de classe. Cet article présente d’abord les changements apportés à la conception de l’apprentissage d’une L2 par les recherches en neurolinguistiques. Puis, il présente les cinq principes majeurs qui constituent les paramètres de l’approche, pour finalement donner un aperçu des stratégies d’enseignement qui en découlent.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".