Sanctuary Movements in Mid-sized Cities in Canada: An Exploration of the Strengths and Limitations of Local Sanctuary Policies and Practices
Bibliographic record
Abstract
Current research on sanctuary city movements in Canada has focused on Toronto, Montréal, and Vancouver. There is a shortage of scholarship on sanctuary policies and practices in nongateway cities, despite the growing presence of sanctuary movements in these areas. This paper explores the strengths and limitations of Canadian sanctuary or access without fear policies and practices in the context of mid-sized cities in Ontario, specifically Hamilton, Ajax, London, and Kitchener. A thematic analysis of policy documents, city reports, and a transcript of a special council meeting is conducted to help determine the strengths and limitations of local sanctuary. The findings indicate that local sanctuary policies are limited because of the constraints of municipal governments related to their legislative authority and structure. Moreover, as an internal policy, sanctuary only influences city-run services and city staff, leaving the conduct of local law enforcement largely unaffected. While local sanctuary has many limitations, it remains a meaningful designation. The policy highlights the reality of non-status or precarious legal status residents and envisions belonging and inclusion in the city beyond one's immigration status.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".