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Record W4229944176 · doi:10.32920/ryerson.14656743

Justice among institutions : the IRB as a component of Canadian refugee status determination

2021· preprint· en· W4229944176 on OpenAlexaffabout
Ian Yuting Lin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEuropean and International Law Studies
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsRefugeeTribunalPolitical scienceLawImmigrationPublic administrationAdversarial system

Abstract

fetched live from OpenAlex

This paper takes an institutional approach to examine justice in Canadian refugee status determination, focusing on the Immigration and Refugee Board (IRB) as an administrative tribunal. The IRB is viewed in the historic context of post-Second World War international rights expansion and the rise of New Public Management as an administrative paradigm. Policies implemented by the recent Harper governments are reviewed in light of the IRB’s high permeability to executive influence and low judicial intervention; issues undermining the IRB’s substantive independence are discussed; the interaction of the IRB with other institutions in Canadian refugee status determination, such as the IRCC and CBSA, are examined in terms of venue shopping for implementing desired policy. The possibility of integrating adversarial-style hearings into the IRB while maintaining its currently centralized research and jurisprudence is proposed. Keywords: separation of powers, refugee status determination, Immigration and Refugee Board of Canada, administrative tribunal, rights expansion, managerialization, New Public Management, endogeneity of law, executive permeability, judicial intervention, venue shopping, inquisitorial hearing, adversarial hearing.

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)0.008
metaresearch head score (Gemma)0.027
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 score0.111
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0260.031
Scholarly communication0.0140.004
Open science0.0020.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.093
GPT teacher head0.360
Teacher spread0.267 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same topicEuropean and International Law StudiesFrench-language works237,207