The Canadian Multicultural Education Policy Web: Lessons to Learn, Pitfalls to Avoid
Bibliographic record
Abstract
Canada prides itself on being the first country to coin the term “multiculturalism” and on being the first country to adopt a multiculturalism policy. This article examines recent moves in Canadian policy related to multiculturalism and multicultural education that are aimed at changing the direction of the policy away from an emphasis on diversity as a strength of Canada to diversity as a problem that needs to be overcome. We use an analytical approach called the “policy web” (Josheeq & Johnson, 2005) to illustrate how the shift from social justice to social cohesion has resulted in a very different understanding of the meaning and possibilities for multiculturalism and multicultural education. We conclude that while there are things to learn from the Canadian example, we caution others to think carefully about some of the new directions.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".