Quel travail sur les textes littéraires dans les écoles québécoises de l’entrée à la sortie du secondaire ?
Bibliographic record
Abstract
Dans cette contribution, nous partageons, tout d’abord, notre vision de la spécificité du rapport aux textes littéraires en contexte scolaire. Après cela, nous proposons un regard analytique sur la manière dont les programmes de français québécois des ordres d’enseignement primaire, secondaire et collégial encadrent la lecture des textes littéraires et le travail effectué autour de ces textes, dont le mode d’approche à privilégier, en essayant de dégager certains indices de progression à travers les différents ordres et cycles d’enseignement. En écho aux éléments du curriculum prescrit, nous présentons les résultats saillants d’un ensemble de données empiriques recueillies dans le cadre de plusieurs recherches étalées sur les vingts dernières années et portant principalement sur les pratiques déclarées des enseignant·e·s du primaire, du secondaire et du collégial.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".