Diverging Policy Approaches to Diversity in a Bi-National Country: The Case of Canada
Bibliographic record
Abstract
This article deals with Canada’s policy approach to immigration- and minority-related diversity in light of its federal structure and the contrast between the predominantly French-language province of Québec and the mainly English-speaking rest of the country, with a particular focus on the province of Ontario. While the two parts of the country share many common features, some contrasts are quite significant. Canada is bilingual at the federal level, but French is Québec’s only official language and the Charter of the French Language, which regulates the use of language in many areas of social life, has constitutional status in that province. A long-standing agreement lets Québec handle the selection of its own immigrants with a similar system than the one used by the federal government for Ontario and other provinces, but with different weighing assigned to language skills. Also, religious diversity is treated differently in the two Canadian provinces, on account of diverging views on secularism, even if both share a public commitment to the protection of minorities. Likewise, there is a difference in their policy approaches regarding the promotion of cultural expressions and the arts, partly because of the French-speaking people’s nationalist outlook. In sum, Canada’s case demonstrates that a country can embrace more than a single approach to diversity. Québec has taken a different path and, in a way, showcases a “third way” between North American multiculturalism and European-like integrationism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.055 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".