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Record W2295142193 · doi:10.29173/alr780

Implications of Case Management and Active Adjudication for Judicial Disqualification

2017· article· en· W2295142193 on OpenAlexaffvenueabout
Jula Hughes, Philip Bryden

Bibliographic record

VenueAlberta Law Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of AlbertaUniversity of New Brunswick
Fundersnot available
KeywordsAdjudicationApprehensionIntervention (counseling)Settlement (finance)Political scienceJudicial reviewJudicial discretionAnticipation (artificial intelligence)LawLaw and economicsPsychologySociologyBusinessComputer science

Abstract

fetched live from OpenAlex

The judicial role of Canadian judges is changing to allow judges to make trials fairer, more accessible, and more efficient. Along with the changing role of judges has come new tools, including pretrial settlement and case management conferences, and even active adjudication during the course of the trial. However, this new role and the use of its associated tools have the potential to raise an apprehension of bias. This article focuses on recent case law and commentary addressing case management and active adjudication by judges, with the aim of clarifying the boundary between permissible judicial intervention that fosters fairness and efficiency, and impermissible interventions that raise an apprehension of bias. Additionally, we discuss the role counsel can play in helping to avoid concerns of bias from arising.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.099
GPT teacher head0.464
Teacher spread0.365 · 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 designNot applicable
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

Citations1
Published2017
Admission routes3
Has abstractyes

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