Challenges for Australian Tribunals; Towards Aretaic Practice Principles
Bibliographic record
Abstract
‘…more citizens… receive their justice from agencies than the courts’ (Chitra: 3). The last 30 years of administrative law practice in Australia have been characterised by the proliferation of tribunals and other bodies engaged in quasi-judicial work. These organisations have developed on a largely ad hoc basis, particularly at the state and territory level. Despite this rapid growth – or perhaps as a result of it - a clear articulation of the underlying practice principles pertinent to tribunals has yet to occur in any coherent fashion. This thesis canvasses the current mechanisms used to assist tribunal members in their work, including legislative and ethical parameters. The argument is then mounted that more guidance is needed in relation to the normative excellences of the tribunal member’s role. An analysis of the systemic and cultural challenges faced by the sector is presented, highlighting the lack of sector definition, disparate practices, resource limitations and institutional isolation that characterise Australian tribunal work. Canadian and United Kingdom experiences provide a useful context for Australian impediments and developments. A state-based merits review body is used as a case study throughout the thesis to illustrate these issues. It is posited that greater emphasis upon the philosophical underpinnings of tribunal practice will assist in improving cohesion among sector members, leading to enhanced delivery of administrative justice. A stance from virtue ethics is adopted as a starting point for this endeavour, on the basis that improved decision-making commences with close analysis of the excellences defining the tribunal member’s role. This aretaic approach highlights the importance of ‘practical wisdom’ or phronesis in tribunal work. Methods for implementing and enhancing discussion of relevant tribunal practice principles are then examined, with various innovative legal training tools being nominated as particularly useful in the tribunal context. The tribunal sector is generally seen to have operated adequately to date. A more normative approach to decisionmaking excellence within the tribunal field is required, however, with this thesis forming the staring point of such analysis.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.016 | 0.017 |
| 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".