What’s Language Got to do with it? An Exploration into the Learning Environment of Quebec’s Classes d’Accueil
Bibliographic record
Abstract
This article stems from an on-going qualitative study of the ‘environment’ of Montreal’s elementary level welcome classes for new immigrant students (classes d’accueil), including teachers’ language attitudes and actual language practices in the classroom. Since the official language of instruction in Quebec is French, the classe d’accueil provides a unique setting for exploring many issues: how teachers look upon the linguistic and cultural diversity of their learners; how teachers negotiate their way between potentially opposing tensions-- to integrate newly arrived children into Quebec, and, to reinforce Quebec’s distinct cultural and linguistic status. By drawing on socio-cultural theory of language learning, this study explores the manner in which teachers might foster an inclusive learning environment in the classe d’accueil. Cet article est le résultat d’une étude qualitative en cours à l’école primaire qui analyse l’environnement des classes d’accueil pour nouveaux immigrants à Montréal. Elle analyse également les attitudes linguistiques et les pratiques langagières des professeurs dans la salle de classe. Comme la langue officielle de l’éducation au Québec est le français, ces classes d’accueil offrent un environnement unique pour explorer beaucoup de questions : comment les professeurs perçoivent-ils la diversité culturelle et linguistique de leurs apprenants, comment négocient-ils les tensions opposées, c’est-à dire comment font-ils pour intégrer des enfants qui viennent d’arriver au Québec en même temps que renforcer la particularité linguistique et culturelle de la province. Tout en se basant sur la théorie socio culturelle de l’apprentissage des langues, cette étude explore les différentes façons offertes aux professeurs qui encouragent la mise en place d’un environnement d’apprentissage inclusif en classe d’accueil.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".