Refining the Reasonable Apprehension of Bias Test:\nProviding Judges Better Tools for Addressing Judicial\nDisqualification
Bibliographic record
Abstract
Despite a considerable amount of litigation concerning judicial impartiality, the Canadian "reasonable apprehension of bias" test for judicial disqualification has remained fundamentally unaltered and is well accepted in the jurisprudence. Unfortunately, the application of the test continues to generate difficulties for judges who need to use it to make decisions in marginal cases. Based on previously published doctrinal and empirical research, the goal in the present contribution is to suggest modifications to the test that will better explain the existing jurisprudence and make it easier for judges to understand when recusal is or is not necessary in marginal cases. The authors consider first the advantages of the existing test and suggest that in order to be useful, any refinement to the test must, to the greatest extent possible, preserve those advantages. Second, the authors explain why inconsistent application of the test in marginal cases is a concern. Third, they analyze the ways in which the existing test, and the jurisprudence explaining and applying it, are problematic. Fourth, the authors propose a modification to the "reasonable apprehension of bias" test that is designed to address these shortcomings while preserving the key advantages of the existing test.
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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 | 0.062 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".