Bibliographic record
Abstract
Over the last decade, sanctuary has been evoked as an alternative to the problems associated with an exclusionary statist asylum regime. In Canada, the United States, and Europe, a “cities of sanctuary” movement has emerged, articulated through various political vocabularies. This movement conceives of sanctuary not simply as a church-based site where asylum seekers may be secured but offers a host of welcoming practices within and beyond cities. This article specifically explores the UK-based City of Sanctuary movement, with a focus on the case of Glasgow, which has widely been read as exemplifying hospitality toward an empowerment of asylum seekers. It has been argued that while a statist discourse of fear—a “politics of unease”—posits migrants as a threat to be policed, the City of Sanctuary stimulates a softer approach. Yet, this article illustrates how the City of Sanctuary is also mobilizing a deeply troubling “politics of ease.” Based on an ethnographic investigation, I show how a politics of ease renders intractable the serious problem of protracted waiting that many controls many asylum seekers. In doing so, I demonstrate how the seemingly hospitable City of Sanctuary in fact contributes to a hostile asylum regime by indefinitely deferring and even extending a temporality of waiting.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".