La grille critériée : toujours une bonne méthode d’évaluation ? Comparaison de différentes méthodes d’évaluation des éléments paraverbaux dans les productions orales d’élèves de 11-12 ans
Bibliographic record
Abstract
Cet article compare trois méthodes d’évaluation des aspects paraverbaux dans les productions orales : la méthode holistique absolue (note globale), la méthode analytique absolue (grille critériée) et la méthode holistique comparative (logiciel Comproved). Trois questions principales guident l’étude : 1) Quelle est la fiabilité inter-évaluateurs de chaque méthode ? 2) Quelle est la corrélation entre ces méthodes ? et 3) Quels écarts de notation observe-t-on entre elles ? Chaque méthode a été utilisée pour évaluer des productions orales sur des critères paraverbaux tels que l’intonation, le volume et les pauses. Les résultats révèlent que, contrairement aux attentes, la méthode holistique absolue présente la meilleure fiabilité inter-évaluateurs. Bien que des corrélations significatives existent entre les méthodes, des écarts de notation importants subsistent. Ces résultats remettent en question l’utilisation systématique des grilles critériées et montrent qu’il est crucial d’adapter les méthodes d’évaluation aux objectifs spécifiques, notamment pour les aspects paraverbaux des productions orales.
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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 | 0.028 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".