Revising Canada's Ethical Rules for Judges Returning to Practice
Bibliographic record
Abstract
It has recently become more common for retired Canadian judges to return to the practice of law. This development raises an array of ethical considerations and potential threats to the integrity of the administration of justice. Although most codes of legal ethics contemplate the possibility of former judges returning to practice, the rules on this particular topic are dated, under-analyzed, and generally inadequate. This article reviews the Canadian ethical rules that specifically relate to former judges and identifies their shortcomings. In doing so, the authors consider, for comparative purposes, Canadian ethical rules directed at former public officers who return to practice and American rules directed at former judges. These rules have been developed in a different context, but involve many of the same issues and are more comprehensive. Following this analysis, the authors propose a series of new rules for judges who return to practice. These rules are not intended as the final word on the subject, but rather as starting points for further discussion of the issues involved. They illustrate the competing considerations with which law societies need to grapple as more judges return to practice.
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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 | 0.056 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".