L’intégration de la musique et du français au programme d’immersion française : avantages pour l’apprentissage de ces deux matières
Bibliographic record
Abstract
Cet article traite de l'intégration de la musique au programme de français langue seconde et de son influence sur l'apprentissage de la musique et du français. Un programme « musique-langue » qui vise l'apprentissage des concepts de patrons (patterns) mélodiques-rythmiques et de forme musicale offrant des similitudes importantes avec la compréhension et la production orales et écrites du français a été expérimenté par les titulaires de classe. Six classes en deuxième année d'immersion française au Nouveau-Brunswick ont formé l'échantillon: groupe contrôle (n = 64), groupe expérimental (n = 63). Tous les sujets ont subi des prétests et des post-tests. L'application des procédures statistiques MANOVA et MANCOVA révèle une différence significative entre les deux groupes lors des tests de musique et de productions orale et écrite du français.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".