The False Dichotomy Between Regional Representation and Other Forms of Diversity: Reimagining a Representative Court
Bibliographic record
Abstract
The 2016 Ivan C. Rand Memorial Lecture was given in the immediate wake of the appointment of Justice Malcolm Rowe of Newfoundland and Labrador to the Supreme Court of Canada which allayed many fears concerning the elimination of Atlantic Region representation at the Supreme Court of Canada. Professor Peter H. Russell spoke of the inception of regional representation and the transient reconstruction of the appointment of Supreme Court justices which, in the wake of the new Trudeau approach, inspires further questions of what exactly a Supreme Court justice should be and where regional repre-sentation fits amidst the structure of the judiciary whilst also working towards diversity. La Ivan C. Rand Memorial Lecture 2016 fut prononcée immédiatement après que le Juge Malcolm Rowe de Terre-Neuve-et-Labrador fut nommé à la Cour suprême du Canada. Cette nomination élimina plusieurs inquiétudes entourant la représen-tation de l'Atlantique à la Cour suprême. Le Professeur Peter H. Russell a discuté du rapport entre la représentation régionale et la reconstruction du processus de nomination des juges de la Cour suprême du Premier Ministre Trudeau et les questions soulevées par les changements et les circonstances entourant nos concep-tions du juge idéal pour siéger à la Cour suprême et la place de la représentation régionale au sein de la structure judiciaire tout en travaillant vers la diversité.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.028 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".