The Battle for the Wrong Mistake: Risk Salience in Canadian Refugee Status Decision-making
Bibliographic record
Abstract
Canadian refugee status adjudicators must choose between two opposing bodies of law, one of which resolves doubt in the claimant’s favour and the other at the claimant’s expense. How do they decide which to prefer? How do they decide whether it would be better to risk accepting an unfounded claim or to risk rejecting a well-founded one? This paper explores one potentially relevant factor: the salience of the harms that decision-makers associate with potential risk outcomes. A brief account of recent events in Canadian refugee law history, beginning with the refugee law reforms of former Conservative Immigration Minister Jason Kenney, shows how risk salience can be manipulated. For each refugee claim to be heard on its own merits, the law cannot leave adjudicators to decide for themselves which kind of error to prefer. It must recognize that sending a refugee home to persecution is the wrong mistake.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".