The Forms and Limits of Judicial Inquiry: Judges as Inquiry\nCommissioners in Canada and Australia
Bibliographic record
Abstract
In both Canada and Australia the conduct ofpublic inquiries draws heavily from the expertise of the legal profession, with judges frequently serving as commissioners and inquiry hearings often reproducing the popular imagery of a courtroom. Despite this affinity between public inquiries and the legal profession, however, jurisprudential and academic authorities repeatedly stress that public inquiries are non-adjudicative. Indeed, the received wisdom is that the investigative focus of public inquiries justifies their divergence from the procedural and substantive commitments of adjudication. This paper challenges that assumption. It argues that the service of judges as inquiry commissioners should be premised on their fidelity to the basic value ofadjudication, a commitment necessary both to honour the due process rights of inquiry participants and the constitutional principle of separation of powers. Drawing from constitutional jurisprudence, practical examples of judicial service on inquiry commissions in Canada and Australia, and an understanding of adjudicative processes from the perspectives of their participants, I propose an analytic method to resolve the unique dilemmas faced byjudges as inquiry commissioners. This method speaks directly to the ethics of judges, reinforcing a connection between their skills, procedural methods, and commitment to honour the basic principles of a just legal system.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.040 | 0.047 |
| Scholarly communication | 0.027 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".