Risk and the Reasonable Refugee: Exploring a Key Credibility Inference in Canadian Refugee Status Rejections
Bibliographic record
Abstract
Abstract This mixed-methods study analyses a sample of 303 rejections of refugee claims by Canadian refugee status adjudicators. It explores the role that inferences about the claimant’s risk response play in supporting the adjudicators’ conclusions that the claimant is lying. In justifying their negative credibility conclusions, the adjudicators in almost two out of three decisions (63%) cited the claimant’s risk response. They often measured the claimant against a general idealized standard: in the face of an alleged danger, the claimant did not act like a ‘person at risk’. This approach brings to refugee law the confusion that characterizes the common law’s most famous fiction. Like the ‘reasonable man’, the ‘person at risk’ blurs the lines between descriptive analyses aimed at understanding how a person would have acted and normative analyses aimed at establishing how a person should have acted. Moreover, in deciding how a ‘person at risk’ would act, the adjudicators did not consider social scientific sources. For many decades, researchers have investigated how human beings respond to potentially deadly threats such as natural hazards, lethal illnesses, attacks, and assaults. The adjudicators’ reasoning, resting on common sense alone, often ran counter to key insights that emerge from this body of research. This study’s findings suggest that refugee systems must guard against the use of normative standards in drawing credibility inferences from a claimant’s risk response, and that they must do more to ensure that social scientific evidence informs these judgments. Evidence about human risk response should be on the record in every refugee hearing.
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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 | 0.040 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".