Enseigner la littérature numérique au secondaire, entre innovation et sédimentation : analyse de cas autour d’une recherche collaborative
Bibliographic record
Abstract
L’article vise à montrer comment quatre enseignantes du secondaire français et québécois ont fait évoluer leurs pratiques, découvrant et enseignant un objet nouveau pour elles, la littérature numérique. Deux objectifs sont notamment poursuivis. D’une part, l’analyse se propose de décrire comment les enseignantes se sont appropriées ce nouvel objet d’enseignement à partir de savoirs existants et nouveaux ainsi que d’expériences lectorales personnelles. Seront notamment observés la prise en compte des spécificités technolittéraires de l’œuvre numérique et les choix ayant entrainé des évolutions — innovations et recompositions — dans leurs planifications et pratiques pédagogiques. D’autre part, l’article étudie comment le dispositif de recherche — lui-même innovant — a pu favoriser les déplacements identifiés dans les pratiques et réduire le sentiment d’insécurité qui est souvent associé au changement (Marsollier, 1999).
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".