The Elusive Search for Accountability: Evaluating Adjudicative Tribunals
Bibliographic record
Abstract
Evaluating the success of adjudicative tribunals is an important but elusive undertaking. Adjudicative tribunals are created by governments and given statutory authority by legislatures for a host of reasons. These reasons may and often do include legal aspects, policy aspects and partisan aspects. While such tribunals are increasingly being asked by governments to be accountable, too often this devolves into publishing statistics on their caseload, dispositions, budgets and staffing. We are interested in a different and more basic question – are these tribunals successful? How do we know, for example, whether the remedies ordered by a tribunal actually do advance the purposes for which it was created? Can the success of an adjudicative tribunal be subject to meaningful empirical validation? While issues of evaluation and accountability cut across national and jurisdictional boundaries, the authors argue that this type of question can only be addressed empirically, by actually looking to the practice of a particular board or boards, in the context of a particular statute or statutes, and in particular jurisdictions at particular times. Such accounts can and should form the basis for comparative study. Only through comparative study can the value and limitations of particular methodologies become apparent. This study takes as its case study the role of adjudicative tribunals in the health system. The authors draw primarily from Canadian tribunal experience, though examples from other jurisdictions are used to demonstrate the potential of empirical evaluation. The authors discuss the relative dearth of empirical study in administrative law and argue that it ought to be the focus of the discussion on accountability in administrative justice.
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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 | 0.454 | 0.777 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".