Denying Refugee Protection to LGBTQ and Marginalized Persons: A Retrospective Look at State Protection in Canadian Refugee Law
Bibliographic record
Abstract
Canadian refugee law has evolved substantially in the last two decades, especially when it comes to making findings of whether a state provides protection to a refugee claimant. Canadian refugee law is making problematic presumptions, increasing burdens on claimants, asking claimants whether they have done their due diligence, and accepting just the best efforts of a state to assuage concerns that protection is necessary. All of these measures have been adopted without consideration as to the true object and purpose of the refugee protection regime and the international law principles that support this regime. Currently in Canada, there is not only confusion as to what the refugee definition calls for in terms of assessing state protection but also the inconsistent application of various approaches, which gives rise to concerns that legitimate refugee claimants are being denied protection, especially those who make claims based on gender and sexual orientation. This article argues that the integrity of Canada's refugee protection regime is at stake and decision makers and judges should reconsider the path Canada has taken since Canada v Ward. The recommendation in this article is to eliminate presumptions, do away with the expectations that claimants need to exercise all due diligence, and accept nothing but effective state protection.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".