Bibliographic record
Abstract
From its development in the 1980s, the sanctuary city movement—municipal protection of people with uncertain migration status from national immigration enforcement—has been a powerful and controversial side of progressive migration policy reform. While some migration activists view sanctuary city policy as the most important aspect of their work, others see it as actively impairing efforts in the fight for migrant rights. In Making Sanctuary Cities , Rachel Humphris provides a new understanding of how citizenship is negotiated and contested in sanctuary cities and what political potentials are opened (and closed) by this designation. Through long-term fieldwork across the sanctuary cities of San Francisco, Sheffield, and Toronto—three of the first municipalities to adopt this designation in their respective countries—Humphris investigates the complexity of sanctuary city policy. By capturing the wide-ranging meanings and practices of sanctuary in comparative context, Humphris uncovers how liberal citizenship is undermined by the very thing that makes it worth investing in: the promise of equality. Attending to the tensions inherent in sanctuary policy, this book opens vital questions about the ways governing systems can extinguish political ideals, and how communities choose to live and organize to fight for a better world.
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".