Re-connaître les plans à cas unique en sciences de l’éducation
Bibliographic record
Abstract
En éducation, la recherche devient incontournable tant pour les enseignants, que la professionnalisation du métier dirige vers une pratique inspirée des preuves, que pour les administrateurs, que la réduction des ressources oriente maintenant vers la reddition de comptes. Or, il est une méthode peu connue qui pourrait faciliter le rapprochement recherche/pratique, soit celle des plans à cas unique. Ils consistent à mesurer à répétition un indicateur chez un cas, avant l’introduction d’une intervention et tout au long de celle-ci. La comparaison visuelle entre les mesures prises avant et après l’intervention permet d’en étudier l’impact. À la suite d’une description des plans à cas uniques, cet article présente un exemple appliqué à l’éducation, de même que les principaux avantages et inconvénients de cette méthode.
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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 | 0.053 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".