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Record W2794810810 · doi:10.17645/si.v6i1.1273

Municipal Responses to ‘Illegality’: Urban Sanctuary across National Contexts

2018· article· en· W2794810810 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Inclusion · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Metropolitan University
FundersAlbert-Ludwigs-Universität Freiburg
KeywordsSolidarityCITESUrban policyScope (computer science)Context (archaeology)GeopoliticsPolitical scienceScale (ratio)Public administrationSociologyGeographyEconomic growthUrban planningPoliticsLawCartography

Abstract

fetched live from OpenAlex

Cities often seek to mitigate the highly precarious situation of Illegalized (or undocumented) migrants. In this context, “sanctuary cites” are an innovative urban response to exclusionary national policies. In this article, we expand the geographical scope of sanctuary policies and practices beyond Canada, the USA, and the UK, where the policies and practices are well-known. In particular, we explore corresponding urban initiatives in Chile, Germany, and Spain. We find that varying kinds of urban-sanctuary policies and practices permit illegalized migrants to cope with their situations in particular national contexts. However, different labels, such as “city of refuge,” “commune of reception,” or “solidarity city” are used to describe such initiatives. While national, historical, and geopolitical contexts distinctly shape local efforts to accommodate illegalized migrants, recognizing similarities across national contexts is important to develop globally-coordinated and internationally-inspired responses at the urban scale.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.414
Teacher spread0.373 · 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