Canadian Cities and Global Migration: Comparing Local Responses to Demographic Change
Bibliographic record
Abstract
The paper examines how municipal governments in six Canadian cities have adapted their corporate policies, programs and structures to address immigration, settlement, and ethnocultural diversity issues. The audit is based on interview and documentary sources, and provides the database for the development of a three-dimensional typology that classifies cities according to: the normative premises underlying the recognition or non-recognition of immigration and ethnocultural differences; the breadth of their initiatives; and the bureaucratic locus of authority for these issues. We found evidence of wide variations in local models of ethnocultural diversity management that can be attributed to differences in political and bureaucratic cultures, rather than to provincial contexts and the size of a community’s immigrant and visible minority population. The paper also examines whether and how international migration has influenced the planning priorities, challenges and responses of municipal parks and recreation officials. It found that no city adopted a monocultural approach by overtly encouraging immigrants to assimilate into the Canadian sports and recreation culture, or by rejecting accommodation requests outright. The strategies and concrete responses they have adopted reflect civic universalist (e.g. prohibitions against discrimination, program subsidies for the less well-off) and intercultural (e.g. encouraging more interaction between dominant and minority cultural groups in the recreational sphere, encouraging newcomers to participate in “traditional” Canadian sports as well as sports that are popular in their homelands) or multicultural (e.g. special initiatives to address ethnic preferences and needs) philosophies. More structured and proactive approaches to the management of ethnocultural diversity in the parks and recreation policy domain were found in cities where there was corporate support for these initiatives and staffs with professional backgrounds in social work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".