A Ship Without a Captain: Political Disengagement and the Failings of Sanctuary City Policy in Toronto, Canada
Bibliographic record
Abstract
Cities across the world are contending with the human rights and policy consequences of exclusionary national and international migration regimes. Those in federal states have distinctive opportunities to create safe and inclusive (sub-)urban environments and to provide access to sub-national social services, like health, housing, and social assistance. But they also risk provoking hostile reactions from national governments and heated jurisdictional conflicts that can carry serious political and fiscal consequences. This is certainly true of cities in the United States, but it has not been true of Canada whose short history of sanctuary is defined by conflict avoidance. The federal government has not once taken an official stance on the legality of sanctuary city policies, while Mayors and City councils have carved off from broader struggles over the authority to govern migration and borders. This strategy has not served non-status migrants for whom avoiding contact with the federal government is their primary self-interest every day well; above all else, they need municipalities to stand up, intervene, and defend their right to the city. Drawing on empirical research on Toronto, Canada, this chapter reflects on the practical and political failings of localist approaches to sanctuary. Set in the context of struggles for control over data, legal space, and political identity, Toronto’s experience with sanctuary has been defined by the absence of political stewardship marked by an unwillingness to risk conflicts with the federal government. The failings of these policies, and of provincial conceptions of sanctuary, may serve as a lesson to cities in other national jurisdictions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.053 | 0.015 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".