Burdened by Proof: How the Australian Refugee Review Tribunal has Failed Lesbian and Gay Asylum Seekers
Bibliographic record
Abstract
Our argument in this paper is that the evidentiary practices and procedures that have been developed by the Australian Refugee Review Tribunal are operating at a routinely low standard. Such practices contribute to decisions that are manifestly unfair and potentially wrong in law. A recent working paper from the Office of the United Nations High Commissioner for Refugees ('the UNHCR') notes that evidentiary questions have been 'largely ignored in the academic literature.' Our conclusions are drawn from our detailed study of more than 300 refugee tribunal decisions made in Canada and Australia in response to asylum claims brought by lesbians and gay men. Our overall frame of inquiry in this study considers how the respective tribunals grapple with the issue of identity, the complex cluster of dilemmas around the public/private divide, the inability of many decision-makers to imagine the 'other' who stands before them in these claims, and the way this area of law encodes and reflects homophobic stereotyping.
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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 | 0.052 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.051 | 0.024 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".