Les deux usages des orthographes approchées : approche méthodologique et pratique enseignante
Bibliographic record
Abstract
Résumé Le présent article constitue une note de synthèse qui porte sur la présentation des orientations didactiques qui sont susceptibles d’améliorer et de soutenir l’enseignement-apprentissage de l’écrit. L’objectif de cet article est de présenter la démarche didactique des orthographes approchées ainsi que son importance en tant que stratégie qui favorise l’apprentissage de l’écrit et en tant qu’approche méthodologique permettant aux chercheurs de suivre l’évolution de l’écrit chez les élèves. Cette méthode permet aux enseignants de bien s’adapter aux différences individuelles des élèves et de mieux suivre leur cheminement cognitif lors des situations d’orthographes approchées. Mots-clés : orthographes approchées, approche méthodologique, pratique enseignante, enseignement-apprentissage de l’écrit
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".