Seeing like a Zone: Privately deputized sovereignty within Toronto’s Sanctuary City
Bibliographic record
Abstract
Scholarship is split between seeing the Sanctuary City movement in a progressivist light of anti-border civil society movements, or, viewed as another iteration of citizenship controls. Critics point out how Sanctuary Cities, wherein municipalities provide services regardless of immigration status, do little to guarantee security for undocumented peoples who are at constant risk of deportation. Those who are optimistic about the movement’s emancipatory potential celebrate Sanctuary Cities’ ability to challenge the policing of migration. Why are the interpretations of Sanctuary City policies so polarized? I argue Sanctuary City literature suffers from trying to resolve the contradictions of state-based citizenship, devolving the challenge to the city, thus obscuring how state officials, business, and civil society actors can each possess local sovereignty control over urban space. My paper develops the metric of ‘private deputized sovereignty’ to trace how local policy discretion can implement or contest control over citizenship enforcement powers. I investigate how ‘private deputized sovereignty’ emerges from zoning technology inherent to urban spatial production. Conceptually, I introduce ‘seeing like a zone’ as a heuristic to challenge methodological nationalism and cityism which assume sovereignty resides with corporeal structures. In applying zoning analysis to Toronto’s Sanctuary City policy, the paper identifies economic and sanctuary zones where jurisdictional exceptions empowers local authorities, civil society, and/or private actors to either grant amnesty or exile migrants. Toronto being located within an immigrant federal state and being a global city offers a case for multiscalar analysis where migrants’ well-being and harm depends on the ‘privately deputized sovereign’s’ zoning choices in workplaces, healthcare, schools and the street.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".