Official minority‐language education policy outside Quebec: The impact of Section 23 of the Charter and judicial decisions
Bibliographic record
Abstract
Abstract: This article argues that Section 23 of the Charter, litigation and judicial decisions have played key roles in expanding and homogenizing official minority‐lanpage education (omle) policy outside Quebec. The importance of looking beyond Charter jurisprudence to the broader policy impact of litigation and judicial decisions is revealed. The Supreme Court'sMahé decision was particularly important in putting omle policy on the agenda and for providing Francophone groups with important legal, political and symbolic resources that were effectively exploited to generate policy change. Sommaire: Ce texte soutient que les litiges et la jurisprudence déoulant de l'article 23 de la Charte ont joué un rôle essentiel dam l'élargissement et l'uniformisation de la politique relative à l'enseignement dans la langue de la minorité en dehors du Quebec. Il révèle l'importance de voir au‐delè de la Charte quelles ont été les répercussions des litiges et de la jurisprudence sur la politique dans son ensemble. La dkision rendue par la Cour suprême dans l'affaire Mahé a été particulierement importante en mettant à l'ordre du jour la politique de l'enseignement dans la langue de la minorité et en offrant aux groupes francophones d'importantes ressources juridiques, politiques et symboliques dont ils ont effedivement su tirer parti pour engendrer une modification de la politique.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".