Quelles stratégies pédagogiques participent au développement de la compétence scripturale ? Analyse secondaire d’une enquête à grande échelle
Bibliographic record
Abstract
Pour connaitre les stratégies pédagogiques utilisées en classe de français au secondaire québécois qui participent au développement de la compétence scripturale des élèves, nous effectuons 1 une analyse secondaire de données collectées par le groupe Description internationale des enseignements et des performances en matière d’écrits (DIEPE, 1995) (1815 élèves de troisième secondaire du Québec, 300 enseignant⋅e⋅s de français). Aucune stratégie pédagogique ne prédit, avec un haut degré de confiance, l’écriture de textes de meilleure qualité par les élèves (régressions multiples à deux niveaux), mais faire écrire beaucoup les élèves pourrait être lié à de meilleures performances.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".