Bibliographic record
Abstract
Se poser la question de la construction du concept par les eleves, c'est interroger l'enseignement de la grammaire et par la-meme c'est revenir a la linguistique du verbe. C'est ce que propose cet ouvrage qui fait suite au colloque organise les 30 et 31 mai 2012 a l'Institut universitaire de formation des maitres (IUFM) de l'Universite Claude Bernard a Lyon. Le present ouvrage voit le jour grâce au travail de l'equipe Episteverb (nee en 2009) qui se compose de neuf enseignants-chercheurs de differentes universites francaises et canadienne. Ces differents chercheurs se sont reunis autour d'un meme objet d'etude au centre de leurs preoccupations : le verbe, afin d'en explorer la complexite dans ses differentes approches (linguistique, socio et psycholinguistique et didactique). Les recherches respectives des membres de l'equipe font apparaitre le besoin de penser une didactique de la grammaire, plus particulierement celle du verbe, davantage centree sur les savoirs en construction de l'eleve : comment l'eleve construit-il et comment fait-il evoluer sa representation du verbe, puis sa comprehension du fonctionnement morphologique et syntaxique d'un element central de la langue francaise ? Quel est l'impact de l'enseignement recu sur cette evolution ? Les contributions publiees dans ce volume s'attachent donc a denouer peu ou prou la complexite linguistique de la notion de verbe, et sa difficile transposition dans l'enseignement de la langue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".