Comment bien choisir ses instruments d’évaluation et de formation à l’autodétermination ?
Bibliographic record
Abstract
L’importance de déployer des pratiques éducatives soutenant l’émergence et le maintien d’actions autodéterminées n’est plus à prouver. Le défi majeur pour l’ensemble des professionnels est actuellement celui de choisir les instruments adéquats, tant pour évaluer que pour former à l’autodétermination, selon les caractéristiques spécifiques des situations à accompagner. Cet article propose un ensemble de réflexions permettant de guider le choix d’un outil d’évaluation et/ou d’un support de formation de bonne qualité. Il présente aussi trois outils d’évaluation (l’échelle du LARIDI ; le Questionnaire de Choix et l’Inventaire de l’autodétermination) et trois programmes d’intervention (C’est l’avenir de qui après tout ; C’est Ma Vie. Je la choisis !; le Manuel de formation à l’autodétermination et à la participation citoyenne de la personne présentant une déficience intellectuelle) particulièrement intéressants et disponibles dans les régions francophones.
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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 | 0.239 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".