A Mixed Bag: Critical Reflections On The Revised Ethical Principles For Judges
Bibliographic record
Abstract
In 2021 the Canadian Judicial Council completed a multi-year review and update of Ethical Principles for Judges (EPJ), the ethical and professional guidance for all federally-appointed judges in Canada. The revisions address issues such as case management and settlement conferences, technological competence and the use of social media, interactions with self-represented litigants, professional development for judges, confidentiality, and the return of former judges to the practice of law. In this article, five directors of the Canadian Association for Legal Ethics/Association canadienne pour l’éthique juridique analyze the revised EPJ and offer their observations.\nThe article covers five important topics. On impartiality, it explains the ways in which the revised EPJ represents a significant evolution in the understanding of this important concept. The article then critically examines the absence of any reference to Reconciliation. On judicial involvement with the community, it argues that the revised EPJ may lead judges to disengage from community activities to an unwarranted degree and critiques the scope of new provisions requiring judges to avoid visible signals of support for causes or views. On judicial technological competence, the article endorses new obligations but cautions that these developments will have to be supported by significant resources to provide appropriate training and guidance on best practices. On confidentiality and return to practice, the article welcomes the new provisions while highlighting some additional issues including avenues for enforcement.
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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 | 0.183 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.047 | 0.092 |
| Scholarly communication | 0.040 | 0.021 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.040 | 0.089 |
| Insufficient payload (model declined to judge) | 0.001 | 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".