Implications of Case Management and Active Adjudication for Judicial Disqualification
Bibliographic record
Abstract
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.
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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 | 0.134 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.039 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".