Bibliographic record
Abstract
This article calls for a renewed commitment to judicial education on the roles that gender, race, class and other biases can have on judicial decisions and impartiality. This article also calls for the appointment of a more representative and diverse judiciary. An explosion of activity occurred for about a decade between the late 1980s until the late 1990s to promote and implement social context education for judges to help judges understand the realities of people most unlike themselves, and to appoint judges to be more representative of the population of Canada. But this trend has diminished to the point that judicial gender and other forms of bias are now rarely talked about or included in judicial education curricula. Judicial appointments once again are tilted sharply in favor of white male partners in large law firms. This article argues that this disparity raises valid concerns about judicial impartiality, and new concerns about equality and discrimination are beginning to emerge. The first part of this article discusses the history of judicial education in Canada and the leadership role the Canadian judiciary took in creating and developing groundbreaking judicial education programs on social context issues both in Canada and internationally. The second section discusses case law since 2000, critiquing it for the paucity of social context analysis and preference for white male judicial appointments. The conclusion calls for a renewed effort to create socially relevant judicial education in current times and for a more representative judiciary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".