Digital internal bordering: surveillance, data-sharing, and the fate of sanctuary cities
Bibliographic record
Abstract
Sanctuary cities worldwide claim to support precaritised migrants in response to exclusionary national policies. This paper is the first of its kind to analyse how digital technologies hinder the efficacy of sanctuary policies, potentially rendering them obsolete. Drawing on comparative evidence from the UK and Canada, it explores how digitally-driven responses to the COVID-19 pandemic by different government levels (local, regional, national) have impacted migrants' rights. Findings reveal that increasing interoperability among population databases has crucially enhanced immigration authorities' capacity to access sensitive data collected by local service providers, facilitating the detection, detention, and deportation of precaritised migrants. These practices of hostile data-sharing weaken pre-existing sanctuary protections that rely on limited cooperation between local and national officials. However, some local actors have deployed fresh counter-strategies, notably building non-interoperable data management infrastructures to ensure safer access to basic healthcare services. While prior research has examined digital transformations in immigration enforcement and welfare systems separately, this paper bridges these debates to reveal how digitisation tightens border controls within everyday urban life and what avenues of resistance remain available to migrant rights advocates.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".