Evaluating Federally Appointed Judges in Canada: Analyzing the Controversy
Bibliographic record
Abstract
This commentary describes our experiences in trying to undertake a judicial performance evaluation of federally appointed judges in Canada. Some respondents were enthusiastic about the project, but others were strongly opposed to it and worried about the effects that our survey would have on judicial independence. After describing the feedback that we received and the fallout from our project, we examine the relationship between judicial performance evaluation and judicial independence. We argue that a well-conceived judicial performance evaluation does not violate judicial independence. We then explore the resistance to judicial performance evaluation in Canada, using a comparative lens. The explanation for this opposition, it seems, lies partly in the broader socio-political context found in common law jurisdictions with parliamentary systems of government and no judicial elections. In our view, opposition to outside academic inquiry from strong elements within the Canadian legal community also forms part of the answer.
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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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.059 | 0.016 |
| Scholarly communication | 0.017 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".