A Canadian Perspective on the Subjective Component of the Bipartite Test for “Persecution”: Time for Re-evaluation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.023 | 0.088 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".