Le rôle de la perception de soi comme lecteur dans le développement de la compétence en lecture
Bibliographic record
Abstract
Un modèle de compétence en lecture intégrant des aspects motivationnels (perceptionde ses difficultés et progrès) et une mesure d’attitudes envers la lecturea été testé dans le cadre des évaluations externes en Belgique francophone. Validéauprès de deux échantillons contrastés d’adolescents, ce modèle confirme le rôledes perceptions de soi comme lecteur sur le développement des attitudes enversla lecture. Ces dernières constituent, quant à elles, une charnière en lien directavec les performances. Ces résultats amènent à proposer des pistes et des outilsdidactiques incluant un travail sur l’efficacité de ses processus de lecteur et uneanalyse réflexive de ce qui rend un texte difficile pour un lecteur particulier
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".