Excursus: Ambivalences of a Sanctuary City. Rethinking borders: insights into the struggles of Toronto’s ‘Sanctuary City’ policy
Bibliographic record
Abstract
Based on the North American model, Sarah Schilliger examines the successes and challenges that come with official ‘Sanctuary City’ status. With half of its 3 million inhabitants born outside of Canada, Toronto became the first Canadian city to commit to a Sanctuary City policy in 2013, and serves as the blueprint for the German Solidarity City network. Toronto’s Sanctuary City status was the result of a 10-year struggle fought by a broad alliance of civil society organisations. Under the umbrella of the ‘Access without Fear’ campaign, these organisations fought to stop deportations and to achieve residence security and fearless access to legal and social services for people with precarious legal status. Sarah Schilliger shows that a Sanctuary City also requires sufficient budget funds, public awareness campaigns and further education measures for officials and employees of public institutions if the security and protection of precarious status migrants are to remain more than just an empty promise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".