Bibliographic record
Abstract
3 The analysis and opinions in this paper are solely those of the authors and do not represent the position of the IRB, thc Australian Federal Court Of the lARLJ. This paper was a collaborative effor! prepared with the contribution and assistance of others. These people should be acknowledged fOf tbeir substantial contribulion lo the paper. JLL~tice Rod Madgwick. Judge of tbe Federal Court of Australia and Associate Rapporteur ofthe Human Rights Nexus Working Party; James Simeon, Acting Executive Director orthe JARU; RoJfDriver, Federal Magistrate, Law Courts, Sydney, Australia; Dr. Hugo Storey. Senior Jmmigration Judge, United Kingdom Asylum and Jmmigration Tribunal; Patricia Auron, Legal Advisor to the Irnrnigration and Refugee Board of Canada: David Schwartz, Legal Advisor to the Irnmigralion and Refugee Board of Canada; Joan Montgomery, Member, !mrnigration and Refugee Board of Canada; LOfl Scialabba, past Chairperson, United Slates Board of Imrmgration Appeals; Sarah Murpby, Member, Refugee Status Appeals Authority, WeUington, New Zealand; Dalllmm Wamer, Legal Associate Refugee Status Appeals Authority, New Zealand; Professor James Halbaway, University of Michigan, USA and Professor Pene Mathew, Ulllversity ofSydney, Australia. Special thanks go to research officcr oftbe Australia Federal Court, Mr. David Braun for his significant conlribution to the content oflhis paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".