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Record W2994822476 · doi:10.1093/ijrl/eez040

Policing Canada’s Refugee System: A Critical Analysis of the Canada Border Services Agency

2019· article· en· W2994822476 on OpenAlexaffabout
Idil Atak, Graham Hudson, Delphine Nakache

Bibliographic record

VenueInternational Journal of Refugee Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of OttawaToronto Metropolitan University
Fundersnot available
KeywordsRefugeeAgency (philosophy)Context (archaeology)CriminalizationComprehensive Plan of ActionPolitical scienceImmigrationGovernment (linguistics)GeopoliticsEnforcementPublic administrationLawCriminologySociologyPoliticsGeography

Abstract

fetched live from OpenAlex

Abstract The officers of the Canada Border Services Agency (CBSA) play pivotal roles at various stages in Canada’s refugee system, making decisions that are life-changing for asylum seekers. This article examines the evolving institutional setting and processes that define the CBSA’s enforcement policy and its consequences for asylum seekers in Canada. Drawing on the findings of field-research, conducted between October 2015 and May 2018 in three Canadian provinces (Ontario, British Columbia, and Quebec), it argues that the Agency operates in a specific social universe heavily shaped by the post-9/11 geopolitical context of the criminalization of migration. This situation has been exacerbated by the major overhaul of Canada’s refugee system, undertaken by the previous Conservative government in 2012. The article further contends that the way the CBSA has been involved in refugee status determination turns Canada’s refugee system into an adversarial and unfair process for some groups of asylum seekers. To that end, it highlights the CBSA’s policies in three areas: eligibility determination, front-end security screening of refugee claimants, and ministerial interventions at the Immigration and Refugee Board of Canada.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.298
Teacher spread0.293 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations18
Published2019
Admission routes2
Has abstractyes

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