Lire des textes de fiction et des textes informatifs aux élèves du préscolaire et du primaire : analyse des interactions extratextuelles des enseignants
Bibliographic record
Abstract
Cette etude a pour but de comparer les interactions extratextuelles des enseignants du prescolaire et du primaire1 qui font la lecture a voix haute a leurs eleves. Par une methode d’observation structuree, nous les avons observes pendant qu’ils faisaient la lecture d’un texte de fiction et la lecture d’un texte informatif. La complexite de la demande cognitive suscitee par leurs interactions extratextuelles a constitue le principal critere d’analyse selon lequel nous avons etabli des comparaisons. Les resultats demontrent peu de variations entre les interactions extratextuelles des enseignants du prescolaire et du primaire, ce qui mene a conclure qu’ils n’adaptent pas leurs interactions en fonction du niveau de developpement des eleves. Par ailleurs, pour l’ensemble des enseignants, la lecture des textes de fiction amene davantage d’interactions extratextuelles qui suscitent une faible demande cognitive que ne le font les interactions qui accompagnent leur lecture des textes informatifs. Ceci soutient l’importance de varier les genres de textes qu’ils lisent a voix haute aux eleves.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".