'I Simply Do Not Believe': A Case Study of Credibility Determinations in Canadian Refugee Adjudication
Bibliographic record
Abstract
Refugee determinations often turn on a single question: Is the refugee claimant telling the truth? While there are other factors that refugee adjudicators must consider, determining whether the claimant's story is credible remains central to virtually all refugee hearings. In light of the key role credibility assessments play in refugee determinations, scholars are paying increasingly more attention to how refugee adjudicators assess credibility.\nThis article contributes to the growing body of research on this subject by examining the full caseload of one refugee adjudicator at Canada's Immigration and Refugee Board (IRB) over a three-year period. That adjudicator, David McBean, denied all the applications for refugee protection he heard during the first three years of his tenure on the IRB. This article examines McBean's decision-making during this period as a case study to shed light on credibility assessments in Canada's refugee determination process. The case study draws on quantitative and qualitative data obtained from the IRB using access to information requests. While McBean is, as the study demonstrates, an outlier in terms of the frequency with which he denies refugee applications, his reasoning with regard to credibility is nonetheless instructive.\nThe article begins by describing the role of credibility in Canada's refugee determination process, including a discussion of existing scholarship in the area. Next, the article presents the case study, offering a quantitative and qualitative examination of McBean's refugee determinations from 2008 to 2010, with a focus on how he approaches credibility assessments. The article then considers three sets of implications of the case study and ends with a brief conclusion.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.077 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".