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Record W3215771542 · doi:10.4324/9780203128947-11

‘I took up the case of the stranger’: arguments from faith, history and law

2012· book-chapter· en· W3215771542 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicReformation and Early Modern Christianity
Canadian institutionsnot available
Fundersnot available
KeywordsFaithLawPolitical scienceHistorySociologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.964
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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.

Opus teacher head0.050
GPT teacher head0.203
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations12
Published2012
Admission routes1
Has abstractyes

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