Canadian Multiculturalism: Global Anxieties and Local Debates
Bibliographic record
Abstract
In much of the world, and particularly in Europe, there is a widespread perception that multiculturalism has failed, and Canada has not been immune to these rising global anxieties. A number of commentators have argued that smug complacency is blinding Canadians to growing evidence of stresses and failures in ethnic relations in their country. In this article, we explore this evolving debate. We briefly review the global backlash against multiculturalism, and why some commentators see warning signs in Canada as well. We then look at the evidence about how the multiculturalism policy in Canada operates, and about trends in immigrant integration and ethnic relations. We show that there are indeed stresses and strains within Canadian multiculturalism, with real issues that require serious attention. But we misdiagnose the problems, and their remedies, if we read the Canadian experience through the lens of the European debate.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.053 | 0.027 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| 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".