The Merits of “Merits” Review: A Comparative Look at the Australian Administrative Appeals Tribunal
Bibliographic record
Abstract
This article compares several systems of administrative adjudication. In the U.S., adjudication is typically performed by the same agency that makes and enforces the rules. However, in Australia, almost all administrative adjudication is performed by the Administrative Appeals Tribunal [AAT], a non-specialized adjudicating agency, and several other specialized tribunals that are independent of the enforcing agency. These tribunals (which evolved out of concerns about separation of powers) have achieved great legitimacy. In the U.K., recent legislation [the Tribunals, Courts and Enforcement Act] merged numerous specialized tribunals into a single first-tier tribunal with much stronger guarantees of independence than previously existed. An upper tribunal hears appeals from the first tier and largely supplants judicial review. The article concludes by asking whether the U.S. could learn anything from the Australian and U.K. experience and suggests that a single tribunal to adjudicate federal benefits cases might be a significant improvement over the existing model.
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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 | 0.065 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".