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Record W1527792207 · doi:10.25071/1920-7336.21322

A Canadian Perspective on the Subjective Component of the Bipartite Test for “Persecution”: Time for Re-evaluation

2004· article· en· W1527792207 on OpenAlexafffundvenueabout
Michael Bossin, Laïla Demirdache

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsPersecutionPlaintiffRefugeeLawSupreme courtConventionTest (biology)PsychologyPerspective (graphical)Meaning (existential)Asylum seekerPolitical scienceSocial psychologyPoliticsComputer science

Abstract

fetched live from OpenAlex

Canadian decision makers refer so regularly to the bipartite nature of the test for persecution in refugee claims that one rarely gives the matter a second thought. After all, the Supreme Court of Canada in Ward clearly affirmed that a refugee claimant must subjectively fear persecution, and this fear must be wellfounded in an objective sense. In this article, the authors focus on the meaning and validity of the subjective aspect of the bipartite test. It is especially appropriate to do so at this time, given the introduction of the term “person in need of protection” in section 97 of the Immigration and Refugee Protection Act, and recent Federal Court decisions holding that the subjective fear is not a requirement in section 97 cases. Looking at the issue of subjective fear from historical, psychological, and legal perspectives, the authors argue: (a) that the drafters of the UN Convention never intended claimants to be “subjectively afraid” in order to qualify for protection; (b) determining an asylum seeker’s state of mind presents a minefield of potential problems for decision makers; and (c) given the new IRPA provisions dealing with persons in need of protection, the question is not whether there is a bipartite test for determining well-founded fear, but whether, indeed, there ought to be such a test.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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.

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

Citations8
Published2004
Admission routes4
Has abstractyes

Explore more

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