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Record W4292697173 · doi:10.1111/capa.12485

Canadian sanctuary policies in context

2022· article· en· W4292697173 on OpenAlexaboutno aff
Mireille Paquet, Meghan Joy

Bibliographic record

VenueCanadian Public Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsLegitimacyDevolution (biology)Context (archaeology)Psychological interventionRepresentation (politics)Political sciencePublic administrationRegional scienceSociologyGeographyPoliticsLawPsychology

Abstract

fetched live from OpenAlex

Abstract Since 2013, seven Canadian cities have developed sanctuary policies: Vancouver, Edmonton, London, Hamilton, Toronto, Ajax and Montreal. Comparing policy design characteristics, this article points to converging features of these interventions in Canada: a focus on information provision and access as well as a representation of the city as an inclusive and progressive space. The distinctive characteristics of these interventions, as compared to the sanctuary policies in other national contexts, point to the need to consider alternative contextual drivers of local involvement toward this issue: response to long periods of policy devolution as well as use of sanctuary to bolster urban competitiveness and legitimacy for local governments.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.236
Threshold uncertainty score0.886

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0200.008
Scholarly communication0.0090.001
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.000

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 head0.026
GPT teacher head0.269
Teacher spread0.243 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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