‘I took up the case of the stranger’: arguments from faith, history and law
Bibliographic record
Abstract
It may seem surprising that faith groups would offer sanctuary to refused refugees, or material support to undocumented migrants. These acts of resistance and compassion require normally law-abiding moral people to make a conscious choice to defy government and perhaps, if necessary, even break the law. The success of sanctuary movements (defined broadly here) relies on broad public support both to attract willing collaborators, and to forestall government intervention. Previous studies have examined the discourse around sanctuary practice, and the ensuing public debates. This chapter adds to this body of work by offering an empirical study of how individuals and groups publically justified acts of sanctuary; we offer a comparative analysis of these claims in Canada, the United States and the United Kingdom; and finally we attempt to respond, in a limited way, to the challenges raised by these voices. The jurisdictions considered share similar legal, faith and cultural histories, but we seek to understand how their distinct political and geographic contexts shaped their movements. We find that sanctuary practice, and even its very definition; vary widely across these jurisdictions, from shelter in a church, our traditional conception, to rescue in the wilderness. We also discover that sanctuary supporters we heard share a common motivation, and perhaps for those from a Christian worldview, a theological commitment broad enough to encircle all these expressions.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.117 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".