Bibliographic record
Abstract
In Australia, the Refugee Review Tribunal (RRT) provides final merits review of protection visa decisions made by officers of the Department of Immigration and Citizenship (DIAC). The RRT uses an inquisitorial approach which gives its members primary control over and responsibility for the gathering of evidence, including information from the protection applicant and others as well as country conditions information. This approach aims to relieve applicants with limited financial resources and English-language skills and who may have suffered torture and other trauma from the burden of arguing their own case against DIAC or retaining legal counsel to do so. However, it places applicants at the mercy of tribunal members who must have the necessary skills, experience, competence and resources, including time, to conduct the inquiries required to determine an applicant’s status. In light of this fact, judicial decisions releasing RRT members from the obligation to inquire into the plausibility of applicants’ testimony on a crucial aspect of their claim and their mental capacity to give evidence at their hearing are troubling in the refugee protection decision-making context. This article reviews certain aspects of the functioning of the RRT’s inquisitorial approach and ask whether any lessons can be drawn from this experience for Canada’s currently evolving approach to refugee protection decision-making.
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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 | 0.140 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.043 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".