Social Resilience and International Migration in the Canadian City
Bibliographic record
Abstract
This timely volume examines how policies, institutions, and places influence the lives of immigrants and temporary migrants to Canada and how, in turn, those newcomers transform the cities in which they live. Social Resilience and International Migration in the Canadian City draws attention to disparities in outcomes for migrants and proposes strategies to enhance their participation in cities of all sizes. Focused on Ontario and Quebec, chapters pinpoint factors that affect the settlement and integration of immigrants, as well as growing numbers of international students, foreign workers, and refugee claimants. Contributors illustrate how federal, provincial, and municipal policies and diverse institutions – from grassroots churches to settlement agencies – can influence migrants’ capacity to navigate and leverage the resources required to overcome integration challenges. The book’s social resilience framework attends to the social supports that empower migrants to take collective action for their own futures. As migrants interact with a broad range of institutions, those institutions are transformed and become more resilient themselves. Directed at a wide audience of community and government practitioners, migration policy experts, scholars, and civil society activists, Social Resilience and International Migration in the Canadian City provides crucial insight about the policies necessary for helping both migrants and cities thrive, offering ideas for effective implementation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".