Bibliographic record
Abstract
Across North America, Europe and Latin America, multiple sub-state jurisdictions have declared themselves to be migrant “sanctuaries”. By adopting sanctuary status, sub-state jurisdictions signal their welcoming attitude towards migrants as well their opposition to the state-level policies that target them for exclusion. In this article, I examine the place of sanctuary in the broader literature of political resistance and opposition in democratic states, and then whether it can be justified all things considered. I locate my examination in the political theory of federalism, to identify an expectation of cooperation – which, it appears, sanctuary jurisdictions are refusing to accept, usually with respect to immigration enforcement efforts. I refer to this form of opposition as “democratic non-cooperation” and identify its key features. I describe a “cooperation continuum”, to suggest that non-cooperation takes four main forms – evasion, non-engagement, disruption and obstruction – which I describe both in general terms and in relation to sanctuary practices in particular. Finally, I observe that the form of opposition that sanctuary is, is not limited to sanctuary: that is, there are other cases of this form of opposition in other policy domains, and moreover, not all of the objectives taken by those who deploy this form of opposition are progressive. Ultimately, this article's central contribution is to fleshing out modes of opposition in democratic spaces in general, by examining the morality of sanctuary actions taken around the 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".