Bibliographic record
Abstract
Résumé Cet article examine les idéologies linguistiques reliées à l’apprentissage du français langue seconde en immersion française. À partir d’extraits de discours d’élèves, d’enseignants, de parents et d’administrateurs, l’auteure examine les défis qui sont liés aux idées préconçues sur les langues auxquels les jeunes en immersion française font face. La sélection sociale des jeunes pour les différents programmes, la différence entre les programmes d’immersion précoce et tardive, les élèves allophones et la compétence des jeunes seront des thèmes traités. En prenant comme point de départ la sociolinguistique pour le changement, l’auteure pose des questions sur ces thèmes afin de faire réfléchir davantage sur ce qu’ils représentent pour les partis intéressés et comment des changements peuvent être apportés. Abstract This paper focuses on linguistic ideologies related to learning French as a second language in French immersion. Through the analysis of what students, teachers, parents and administrators say, the author looks at challenges faced by students in French immersion. Many of these challenges are related to preconceived ideas on languages. Themes involving the social selection of students in different programs, the differences between early and late French immersion, Allophone students in French immersion, and students’ competencies are explored. Adopting a sociolinguistics for change approach, the author also poses questions related to these themes. Asking questions allows stakeholders to reflect on the issues raised and on how change might be enacted in their own schools or classrooms.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".