Quelles relations entre les compétences lectorales des élèves et les pratiques de leurs enseignants ?
Bibliographic record
Abstract
Cet article présente le troisième volet de la « recherche Gary » qui a été menée de 2015 à 2022 par une équipe de recherche internationale (France, Belgique, Suisse, Québec – cf. Brunel et al., à paraitre). Les deux premiers volets ayant permis de mieux connaitre les compétences des élèves et les pratiques de leurs enseignants, nous nous interrogeons ici, à l’instar de Goigoux dans le cadre de la recherche « Lire-écrire au CP » (2016), sur les relations qu’il nous a été possible d’établir entre ces compétences et ces pratiques. Cette analyse fait bien apparaitre des corrélations significatives entre ces deux ensembles de données. En particulier, elle permet de mettre en évidence des écarts significatifs entre les pratiques des enseignants selon qu’ils s‘adressent à des classes aux résultats plus faibles ou plus forts.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".