Cheminement scolaire d’élèves en difficulté adaptation ou d’apprentissage en enseignement secondaire
Bibliographic record
Abstract
Au Quebec, la politique de l’adaptation scolaire favorise l’integration en classe ordinaire des eleves handicapes ou en difficultes d’adaptation ou d’apprentissage (EHDAA). Malgre l’effort que traduit cette politique pour scolariser ces eleves dans le milieu le plus normalisant possible, quelques etudes demontrent que les jeunes ayant des difficultes d’apprentissage ou d’adaptation ont un cheminement scolaire difficile pouvant mener a des retards scolaires et a un faible taux de diplomation. Dans le cadre d’une recherche statistique descriptive, l’objectif de cet article est de presenter les resultats d’une analyse longitudinale du cheminement scolaire de 15 233 eleves en difficulte d’adaptation ou d’apprentissage (EDAA) ayant frequente l’enseignement secondaire jusqu’a cinq annees apres avoir quitte l’ordre d’enseignement p rimaire, ou ils beneficiaient d’un plan d’intervention actif. Cette etude portant sur les cheminements scolaires montre un recours important aux classes speciales et aux filieres professionnalisantes, et une plus faible diplomation chez cette population. Toutefois, les filles et les anglophones obtiennent des resultats largement superieurs a la moyenne en termes de diplomation.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".