Enseigner les sciences en français langue seconde : Examen d’un programme d’étude au Canada
Bibliographic record
Abstract
<strong>Résumé : </strong> Située dans le cadre du programme d’immersion en français langue seconde, en Colombie-Britannique au Canada, notre contribution se propose d’analyser la manière dont, institutionnellement, l’articulation entre français langue seconde et disciplines est traduite dans les contenus des programmes scolaires provinciaux. À partir d’une analyse documentaire portant sur les documents officiels disponibles sur le site du Ministère de l’Éducation, et plus spécifiquement en étudiant le programme d’étude de Sciences, nous sommes conduits à dégager les choix didactiques opérés au regard de l’intégration de la langue et de la discipline non linguistique, et à examiner la place accordée dans le travail enseignant au développement de la langue seconde face aux contenus disciplinaires dans la mesure où il ressort parfois des tensions entre les savoirs langagiers et disciplinaires. Ce faisant, nous mettons au jour d’une part, les représentations sous-jacentes associées aux objets d’enseignement et d’apprentissage que sont les sciences et la langue et, d’autre part, celles qui ont trait à l’articulation entre disciplines linguistiques et non linguistiques. <strong>Abstract : </strong> Our contribution analyzes how the relationship between French as a second language and the traditional academic disciplines taught in a French immersion program is reflected in the curriculum of the province of British Columbia, Canada. Based on a document analysis of the official documents available on the Ministry of Education website, and more specifically by studying the Science curriculum, we identify the pedagogical choices made with regard to the integration of the language and the non-linguistic discipline. In so doing, we examine the place given in lesson plans to the development of the second language in relation to disciplinary content, noting in particular tensions that arise between linguistic and disciplinary knowledge. We shed light on the underlying representations associated with the teaching and learning of science and language and, more generally, the underlying representations between linguistic and non-linguistic disciplines. Cécile Sabatier Bullock - Faculté d’Éducation, Simon Fraser University, Canada<br /> <a href="mailto:sabatier@sfu.ca">sabatier@sfu.ca</a><br /> <br /> Shawn Michael Bullock - Faculté d’Éducation, University of Cambridge, UK<br /> <a href="mailto:smb215@cam.ac.uk">smb215@cam.ac.uk</a>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".