Risk Theory and 'Subjective Fear': The Role of Risk Perception, Assessment, and Management in Refugee Status Determinations
Bibliographic record
Abstract
Adjudicators deciding refugee claims often assume that people in danger will take prompt and effective steps to save themselves and will never willingly put themselves at risk. They rely on three articles of faith handed down by generations of judges: those who fear for their lives in their homelands will not delay in leaving; they will ask for protection immediately in the first safe country that they reach; and they will never return for any reason. These assumptions are not based on any evidence, and yet evidence is close at hand. For decades, psychologists, sociologists, anthropologists, economists and historians have studied how human beings perceive and respond to danger. This article reviews this research and concludes that before adjudicators could even potentially infer from these types of actions that a claimant was not afraid, or is lying, they must consider the psychological and cultural factors influencing the claimant's risk perception, assessment, and management. It concludes that even when all these factors are taken into account, the well-documented variance in human response to danger makes ‘subjective fear’ judgments fundamentally unsound.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.060 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".