La grammaire dans l'histoire des méthodologies. Exposé et animation d'un atelier
Bibliographic record
Abstract
La question traitée dans cet atelier est celle de la (place de la) grammaire dans l'histoire des méthodologies. Je l'aborderai en quate points avant de lancer la discussion. 1°) Quel est l'intérêt de la question ? Peut-on y répondre ? 2°) Peut-on identifier des « lames de fond » ? 3°) Que faire des notions d'éclectisme et de plurilinguisme 4°) De l'importance d'un métalinguistique contrastive Aperçu bibliographique H. Besse et R. Porquier, Grammaires et didactique des langues, Paris, Hatier-Crédif, 1984 ; H. Besse, Propositions pour une typologie des méthodes de langues, Université de Paris VIII, 1999-2000 ; S. Auroux (éd.), Histoire des idées linguistiques, Liège-Bxl, Mardaga, 1989-2000 (3 t.) ; S. Auroux, La question de l'origine des langues suivi de L'historicité des sciences, Paris, PUF, 2007 ; Ch. Puren, Histoire des méthodologies de l'enseignement des langues, Paris, Nathan-Clé International, 1988 ; Cl. Germain, Évolution de l'enseignement des langues : 5000 ans d'histoire, Nathan-Clé International, 1993 ; J. Caravolas, Histoire de la didactique des langues au siècle des Lumières. Précis et anthologie thématique. Montréal-Tübingen : Les Presses de l'Université de Montréal - Günter Narr Verlag, 2000. Et l'ensemble des travaux de la SIHFLES.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".