MétaCan
Menu
Back to cohort
Record W3021890710 · doi:10.1111/imig.12714

Rescaling the Sanctuary City: Police and Non‐Status Migrants in Ontario, Canada

2020· article· en· W3021890710 on OpenAlexaffabout
Mia Hershkowitz, Graham Hudson, Harald Bauder

Bibliographic record

VenueInternational Migration · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHonourImmigrationLaw enforcementAgency (philosophy)PledgePolitical scienceLawPublic administrationImmigration lawGovernment (linguistics)EnforcementLocal governmentCriminologySociology

Abstract

fetched live from OpenAlex

Abstract Canadian cities pledge to provide access to municipal services to non‐status migrants and withhold information identifying non‐status migrants from federal immigration authorities. Despite these promises, local police continue to cooperate with the Canada Border Services Agency, which raises questions about the capacity of cities to honour their promises. An empirical study involved interviews with high‐ranking police officers in eight local jurisdictions in Ontario about police perceptions regarding their role in the enforcement of federal immigration law and their obligations to honour sanctuary‐city policies. The results show that many police officers believe they possess legal authority to report non‐status migrants to federal authorities. We suggest that this belief rests on misconceptions about the relationship between criminal law and immigration law, claims of jurisdictional immunity from municipal government, and distortions of the principles of policing in Canada. Rescaling of sanctuary policies to the provincial level may offer solutions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0110.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations30
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueInternational MigrationSame topicMigration, Refugees, and IntegrationFrench-language works237,207