Les pratiques d’évaluation orale des enseignantsdu primaire et du secondaire
Bibliographic record
Abstract
Cet article porte sur les pratiques des enseignants en classe en lien avec les apprentissages des élèves. Nous nous centrons plus particulièrement sur leurs pratiques d’évaluation orales individualisées. Notre objectif est de décrire, comprendre et expliquer l’activité de neuf enseignantes dont cinq travaillent à l’école primaire et quatre au collège. Les résultats montrent que leurs pratiques évaluatives ver - bales se caractérisent par des variations (intra et interindividuelles) mais également par un certain nombre de stabilités. À l’aide de séquences vidéoscopées et de traitements statistiques, nous rendons compte de ces variations et de ces stabilités en étudiant leurs corrélations avec le niveau d’enseignement, le statut scolaire et le sexe des élèves concernés.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".