Des analogies entre le raisonnement médical et l'évaluation formative
Bibliographic record
Abstract
La présente contribution examine l’hypothèse de la viabilité d’une analogie entre deux activités au cœur des actions respectives du médecin et du pédagogue, le raisonnement médical et l’évaluation formative des apprentissages. A partir de définitions élémentaires, les auteurs examinent d’abord en quoi le raisonnement médical et l’évaluation formative sont deux démarches cliniques, en soulignant les tensions épistémologiques que recouvre cette notion. Puis, sous la forme de rapprochements comparatifs, ils analysent successivement quelques similitudes ou distinctions à établir entre les deux activités, au regard de plusieurs attributs, notamment leurs objets, leurs processus et les postures qu’ils impliquent. Les analyses font essentiellement référence au raisonnement médical des médecins et l’évaluation formative est plus particulièrement considérée au regard de la manière selon laquelle elle est régulièrement mise en œuvre dans le cadre de la supervision formative en contexte clinique.
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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 | 0.035 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".