Perspective critique sur l’enseignement de la littératie disciplinaire en contexte d’inclusion et incidences sur la planification de l'enseignement
Bibliographic record
Abstract
Cet article porte sur la littératie disciplinaire et propose un modèle intégrateur des interventions pédagogiques facilitant la compréhension et l'appropriation de contenu chez les élèves (Hillman, 2014). Faisant suite à une synthèse des connaissances sur la littératie disciplinaire réalisée par Granger et Moreau (2018), deux recensions permettront de nuancer les apports d'une part de la littératie disciplinaire (Hillman, 2014), et d'autre part de la littératie générale (Flaggella-Luby, Sampson Graner, Deshler et Valentino Drew, 2012) dans la planification de l'enseignement au bénéfice d’élèves en difficulté d'apprentissage. Tout au long de cet article, des exemples sont apportés et quelques recommandations sont formulées dans le but de guider les enseignants qui souhaitent rehausser les compétences en littératie chez leurs élèves de la fin du primaire ou du secondaire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".