Turning the Tables on RDS: Racially Revealing Questions Asked by White Judges
Bibliographic record
Abstract
In the 1997 RDS case, the Supreme Court of Canada deliberated on the concept of judicial race bias. The decision subjected the oral ruling of a lower court trial judge in a busy Youth Court to close scrutiny. The majority of the nine-person, all-white bench reprimanded Canada’s first Black female judge, whose words about police officers who “overreact” in dealing with racialized youth they found “troubling” and “worrisome.” This article places the same close scrutiny on the words of the white judges who were most critical of the trial judge. It examines their informal interjections and comments at the Supreme Court oral hearing. Making use of the appellate transcript and video-recording of the oral argument, it concludes that the informal comments of the top court judges exemplified many of the patterns that anti-racist educators describe as indicative of a lack of understanding of racism.\nDans l’affaire RDS de 1997, la Cour suprême du Canada a délibéré sur le concept de partialité raciale judiciaire. La décision a soumis à un examen minutieux la décision prononcée oralement par une juge de première instance dans un tribunal pour adolescents très fréquenté. La majorité des neuf juges, tous de race blanche, ont réprimandé la première juge de race noire au Canada, dont les propos sur les agents de police qui « réagissent de façon excessive » lorsqu’ils traitent avec des jeunes racialisés ont été jugés « inquiétants ». Le présent article examine avec la même attention les propos des juges blancs qui ont le plus critiqué la juge de première instance. Il examine leurs interjections et commentaires informels lors de l’audience de la Cour suprême. S’appuyant sur la transcription de l’appel et l’enregistrement vidéo de la plaidoirie, il conclut que les commentaires informels des juges de la Cour suprême illustrent bon nombre des modèles que les éducateurs antiracistes décrivent comme révélateurs d’un manque de compréhension du racisme.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".