The role of multiculturalism policy in addressing social inclusion processes in Canada
Bibliographic record
Abstract
As we approach the 40th anniversary of Canada’s multiculturalism policy, the concept of multiculturalism is under attack in many jurisdictions. The leaders of Germany, France and Britain, have each declared that multiculturalism has been a failure in their countries, serving to separate and segregate, rather than integrate (Edmonton Journal, February 13, 2011). It seems timely therefore, to briefly review the origins and evolution of Canada’s multiculturalism policy and examine future directions in light of the changing global and national situation, and newly emerging public discourses on integration, inclusion and the meaning of Canadian identity. The focus of this paper is on the role multiculturalism policy plays in creating a more inclusionary society in the twenty-first century in Canada. We set the context by presenting a brief historical overview of multiculturalism policy since its introduction in 1971 and summarizing some of the recent Canadian discourse surrounding multiculturalism. One of the key questions we explore is whether multiculturalism policy should move beyond focusing on the integration of population groups marginalized by national, racial, religious or ethnic origins, to addressing broader social inclusionary processes that influence inequities and impact on nation. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".