Differences entre les distributions du rendement scolaire au secondaire: le role de la taille de la classe et du temps d'enseignement
Bibliographic record
Abstract
Dans le present document nous avons recours a l'application de la methode de decomposition de DiNardo, Fortin et Lemieux (DFL, 1996) pour ventiler les differences entre les distributions provinciales des notes au test du Programme international pour le suivi des acquis des eleves (PISA) et pour evaluer la contribution relative de la distribution de la et du temps d'enseignement, des autres caracteristiques de l'ecole et des caracteristiques du milieu familial des eleves aux differences interprovinciales de rendement. La taille de la classe et le temps d'enseignement etant deux variables qui ont une influence importante sur le choix de l'ecole, nous examinons comment les differences interprovinciales de rendement scolaire evolueraient si la distribution de la taille de la classe et du temps d'enseignement observee en Alberta etait celle en vigueur dans les autres provinces. Les resultats varient selon la province et, pour celles ou les ecarts moyens de rendement s'amenuiseraient, les nouvelles conditions n'avantageraient pas tous les eleves.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".