Les élèves doués et talentueux au Canada : une étude comparative
Bibliographic record
Abstract
Cet article propose une analyse des politiques provinciales et territoriales des dix provinces et des trois territoires canadiens au regard des élèves doués et talentueux, dressant un portrait comparatif du contexte dans lequel s’inscrit leur scolarisation. Y sont analysés les définitions et les domaines de la douance ainsi que les mesures pédagogiques proposées pour ces élèves. Les résultats de cette étude documentaire montrent d’abord un fort contraste entre les provinces ne reconnaissant pas cette catégorie et celles les reconnaissant. Chez ces dernières, un consensus sur la terminologie utilisée et la définition est observé. En ce qui concerne les mesures scolaires et extrascolaires, la grande majorité des provinces propose des mesures explicitement ou implicitement destinées aux élèves doués. Toutefois, celles-ci sont plus explicites et détaillées dans les provinces reconnaissant formellement cette catégorie d’élèves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".