Diversity, Transparency & Inclusion in Canada’s Judiciary
Bibliographic record
Abstract
The purpose of this paper is to provide a high level overview of some of the issues and stumbling blocks Canada has encountered in building a diverse judiciary. Part 1 of the paper begins by providing a brief overview of the heterogeneous makeup of Canadian society against the homogenous makeup of the judiciary. This will provide a helpful backdrop from which to explore conceptual questions related to the question of why a diverse judiciary matters. Part 2 examines some of the historical questions and milestones in the judiciary related to diversity. Part 3 summarizes the judicial appointments processes and takes a look at Canada’s recent history related to judicial appointments and judicial diversity – specifically judicial appointments under Prime Minister Stephen Harper’s Conservative government and recent moves by the new Liberal government led by Prime Minister Justin Trudeau. The paper wraps up with our thoughts on reforms that might signal greater commitment to diversity and inclusion as essential elements of an effective and independent judiciary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".