Rescaling the Sanctuary City: Police and Non-Status Migrants in Ontario, Canada
Bibliographic record
Abstract
Abstract The sanctuary city movement is aimed at limiting the local enforcement of federal immigration law. Canadian cities have joined this movement by pledging a) to provide access to municipal services without regard to immigration status, and b) to not share information identifying non-status migrants with federal immigration authorities. Despite these promises, local police continue to cooperate with the Canada Border Services Agency (CBSA). Continued cooperation raises questions about the capacity of cities to honour these promises. This paper shares the results of a preliminary study of the policing of non-status migrants in the Canadian province of Ontario. Relying on interviews with high-ranking police officers in eight local jurisdictions, the authors analyze police perceptions regarding their role in the enforcement of federal immigration law as well as their obligations to honour the spirit and the substance of sanctuary city policies. The study reveals that many police officers believe they possess legal authority to report non-status migrants to federal authorities where, in fact, this authority does not exist. The authors argue that this belief rests on a host of misconceptions about the relationship between criminal law and immigration law, claims of jurisdictional immunity from municipal government, and distortions of the historic, foundational principles of policing in Canada. The authors argue that greater protection of the rights and privacy of non-non-status migrants requires at a minimum a rescaling of sanctuary policies to the provincial level, where policing may be subject to more stringent laws and regulations. Keywords: Sanctuary City; securitization of migration; crimmigration; non-status migrants; policing; scale; jurisdiction
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".