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Record W7009898821

The Forms and Limits of Judicial Inquiry: Judges as Inquiry\nCommissioners in Canada and Australia

2014· article· en· W7009898821 on OpenAlexaffabout

Bibliographic record

VenueeYLS (Yale Law School) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCommonwealth, Australian Politics and Federalism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHonourLegal processPublic serviceDivergence (linguistics)Value (mathematics)Service (business)Economic JusticeJudicial reviewLegal research
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.324
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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Same venueeYLS (Yale Law School)Same topicCommonwealth, Australian Politics and FederalismFrench-language works237,207