DISPOSITIFS DÉCLARÉS D’ENSEIGNEMENT DE LA LECTURE AU MOYEN DE LA LITTÉRATURE DE JEUNESSE EN CONTEXTE D’INCLUSION PÉDAGOGIQUE D’ÉLÈVES HDAA DU PREMIER CYCLE DU PRIMAIRE
Bibliographic record
Abstract
Les recherches en lien avec l’enseignement avec la littérature de jeunesse auprès des élèves HDAA ont été réalisées en contexte de classes spéciales. Aucune d’entre elles ne s’intéressaient au contexte d’inclusion d’élèves HDAA dans des classes régulières, ni à décrire les liens entre, d’une part, le type et l’ampleur des dispositifs didactiques d’enseignement avec la littérature de jeunesse et, d’autre part, le nombre d’élèves HDAA inclus dans la classe. Il s’agit de l’objet de ce texte. Le devis est descriptif. Les résultats montrent des différences en fonction de la quantité d’élèves HDAA inclus.USE OF READING INSTRUCTION METHODS INCORPORATING CHILDREN’S LITERATURE IN INCLUSIVE EDUCATION CONTEXTS WITH ELEMENTARY CYCLE ONE HSMLD STUDENTSStudies on the use of children’s literature to teach students with handicaps, social maladjustments, or learning disabilities (HSMLD) have been conducted in the context of special education classes. Yet, none have explored the context of regular classrooms integrating students with HSMLD, nor described the links between the type and extent of pedagogical methods that incorporate children’s literature used to teach and the number of students with HSMLD included in the classroom. This article contributes to remedying this gap. Our methodological approach is descriptive. Results indicate differences in relation to the number of students with HSMLD included in the classroom.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".