Bibliographic record
Abstract
En tant qu’îlot francophone dans une Amérique du Nord essentiellement anglophone, le Québec est souvent considéré comme un modèle dans les questions de politique et d’aménagement linguistiques. Suivant une discussion des processus théoriques concernés, ce chapitre examine les mesures d’aménagement linguistique particulières pour lesquelles le Québec est devenu réputé et qui cherchent à y améliorer le statut du français (aménagement du statut), à assurer son adoption comme langue publique commune par tous les Québécois (aménagement de l’acquisition), ainsi qu’à enrichir la langue et à s’occuper de sa qualité (aménagement du corpus). Dans tous ces domaines, la politique et l’aménagement linguistiques du Québec sont aujourd’hui façonnés par les nouveaux défis posés par l’immigration et la mondialisation, ce qui témoigne d’une créativité et d’une capacité à s’adapter au changement qui font souvent défaut à la politique et à l’aménagement linguistiques d’autres contextes francophones. As a French-speaking island in a predominantly English-speaking North America, Québec is often considered as a model in questions of language policy and planning. Following a discussion of the theoretical processes involved, this chapter examines the particular language-planning measures for which Québec has become well-known and which aim to improve the status of French there (status planning), assure its adoption as a common public language by all Quebecers (acquisition planning), as well as enrich the language and attend to its quality (corpus planning). In all these areas, Québec’s language policy and planning is today shaped by the new challenges presented by immigration and globalisation, demonstrating a creativity and an ability to adapt to change that are often lacking in the language policy and planning of other French-speaking contexts.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".