Lecture littéraire et lecture subjective : des modèles à l’épreuve des données
Bibliographic record
Abstract
Cet article vise à examiner, sur la base de données empiriques collectées dans le cadre du projet « Gary » que nous avons coordonné, dans quelle mesure les modèles de la lecture littéraire et de la lecture subjective permettent de décrire et de comprendre d’une part les commentaires des élèves de deux niveaux scolaires (élèves de 12 ans et 15 ans) et de quatre pays ou régions francophones (la Belgique, la France, le Québec et la Suisse) à propos d’un texte littéraire, et d’autre part les pratiques de leurs enseignants. Il s’agit, ce faisant, à la fois d’éprouver la validité empirique des deux modèles en observant en quoi ils permettent de mesurer la qualité des lectures enseignées et apprises et de prendre appui sur eux pour tracer des perspectives didactiques pour la formation et l’outillage des enseignants.
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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 | 0.060 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.027 | 0.036 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".