Les représentations d’élèves du secondaire de la littérature à l’école : portrait de quatre profils distincts de lecteurs
Bibliographic record
Abstract
Les enjeux entourant l’enseignement de la littérature sont multiples. Quels corpus enseigner, et pour quelles finalités? Qu’est-ce qui caractérise la lecture littéraire dans le cadre scolaire? Comment favoriser un rapport positif des élèves à la littérature? Pour connaitre 1 les représentations des élèves du secondaire sur ces sujets, nous avons procédé par analyse de classes latentes. Quinze indicateurs de représentations ont été soumis aux participants (n=484). L’analyse de leurs réponses a permis de faire émerger quatre profils et d’identifier les facteurs associés à l’appartenance des participants à ceux-ci. Les résultats montrent la diversité des représentations des élèves et soulignent la pertinence de la valorisation de l’investissement subjectif ainsi que de la diversification des corpus littéraires scolaires.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".