A Multi-Level Perspective on the Urban Governance of Multiculturalism in Toronto: A Metropolis in Motion and a Research Agenda
Bibliographic record
Abstract
Like many advanced industrialized democracies, immigration has transformed the ethno-racial composition of Canada. The dominant account of ethno-cultural diversity’s impact on social cohesion and democratic life is negative. Canada’s positive experience with diversity and its approach to ethno-cultural accommodation has become a model of international success. The ways in which Canada’s diversity is manifest has important spatial dimensions that have been under-acknowledged by Canadian political scientists. Canada is both a highly ethno-culturally heterogeneous and a highly urban nation. The ethno-cultural transformation of Toronto, Canada’s largest city and most important immigrant destination, underscores the urban reality of Canada’s evolving ethno-cultural mosaic. The paper examines the role of urban governance arrangements and municipal policies in Canada’s national policy infrastructure to support ethno-cultural pluralism. It compares the core City of Toronto with three outer suburban municipalities within the Greater Toronto Area - the City of Mississauga, City of Brampton and Town of Markham. These municipal communities share a common national, provincial and city-region context, all have exceptionally high levels of immigration, yet they vary in the extent to which they have developed multiculturalism initiatives to accommodate and manage ethno-cultural diversity. The paper uses the urban regime concept to conceptualize and synthesize the different ways Canada’s national multiculturalism policy infrastructure, the city’s political economy and its changing ethno-cultural demographics influence the urban governance of immigration and multiculturalism. The paper concludes with reflections on the possibilities and limitations of city politics and governance’s contribution to an equitable and effective multicultural citizenship model in Canada. Toronto’s dramatic ethno-cultural transformation, the role of urban political processes in managing it, and the variation within the city-region ought to be seen as a call to Canadian political scientists and policy-makers to take the urban dimensions of the country’s ethno-cultural pluralism seriously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".