L’acquisition de la phonologie en français langue seconde : le profil phonologique d’enfants allophones en maternelle
Bibliographic record
Abstract
La ville de Montréal est l’une des villes les plus multiethniques du Canada. Compte tenu de cette diversité, il y a une grande proportion d’enfants qui apprennent deux langues et donc deux phonologies ; la phonologie de leur langue maternelle et celle du français. L’interaction entre ces deux systèmes phonologiques peut influencer le développement de la phonologie de la langue seconde. Le but de cet article est de comparer les compétences phonologiques en français des enfants allophones à celles des enfants francophones unilingues issues d’études précédentes. Nous utiliserons des mesures pour évaluer le pourcentage de consonnes bien produites par les enfants dans une tâche de dénomination d’images et nous analyserons différents facteurs qui peuvent contribuer aux résultats à cette tâche.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".