Immigrant Settlement Policy in Canadian Municipalities
Bibliographic record
Abstract
Canada has, by most accounts, one of the most successful immigration programs in the world, a function of the policies, programs, and services that assist newcomers. Immigrant settlement is a crucial policy field that involves governments, communities, and a range of social forces. Constitutionally, immigration matters are an area of shared jurisdiction, but the federal government has long been the dominant player. Provinces and municipalities, however, are now pushing for an expanded policy role, increased resources, and governance arrangements that recognize the important part they play in immigrant settlement. Drawing on a great many in-depth interviews with government officials and front-line workers, contributors provide a comparative assessment of approaches to immigrant settlement in nineteen Canadian municipalities. This is complemented by a discussion of the federal government's role in this policy field, and by a comprehensive introduction and conclusion, which ground the book historically and thematically, synthesize its key findings, and provide recommendations for addressing the challenges related to intergovernmental cooperation, settlement service delivery, and overall immigrant outcomes. Individual chapters examine the mechanics of public policy-making but also tell a story about diverse and innovative approaches to immigrant settlement in Canada's towns and cities, about gaps and problems in the system, and about the ways in which governments and communities are working together to facilitate integration. Contributors include Zainab Amery (Carleton University), Caroline Andrew (University of Ottawa), Guy Chiasson (Université du Québec en Outaouais), Rodney Haddow (University of Toronto), Rachida Abdourhamane Hima (Government of Canada), Christine Hughes (Carleton University), Serena Kataoka (University of Victoria), Junichiro Koji (University of Ottawa), Warren Magnusson (University of Victoria), Daiva Stasiulis (Carleton University), Erin Tolley (Queen's University), and Robert Young (University of Western Ontario).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.052 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".