Investigating deception findings in Canadian refugee status rejections: legal inferences and psychological assumptions
Bibliographic record
Abstract
This study uses a novel methodology that combines legal and psychological approaches to analyse a large set of Canadian refugee status rejections (n = 120). It distinguishes legal inferences from their underlying psychological assumptions and quantifies both inferences and assumptions in a set of 89 written decisions. Its findings yield new insights that inform the use of social science in the evaluation of deception findings in this field: it identifies the most important categories of legal inference that support these findings (inferences drawn from observations of ‘inconsistency’, ‘non-probative supporting evidence’ and ‘risk response’), and it is the first study to identify the most significant kinds of assumption that underlie the finding that a refugee claimant is lying. These include assumptions that have been observed in previous studies: assumptions about the consistency of truthful and deceptive accounts and about how people act when they are at risk. Perhaps most importantly, this study has identified a new and significant category of psychological assumption operating in these decisions: assumptions about the robustness of a claimant’s metacognition, their ability to understand and explain their own cognitive processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".