Bibliographic record
Abstract
Should Canada have a representative Supreme Court? While the idea has an undeniable appeal, and is received wisdom in a variety of institutional settings in the public sphere (the federal public service, for example, is committed to becoming a representative institution), it sparks some questions. What groups or values should judges represent and how should they represent them? By what criteria should representation be assessed and in whose eyes? Finally, what does representation mean to and for the Supreme Court, and those affected by its decisions?This set of interlocking questions must be asked against the backdrop of two broader debates in Canada – one surrounding judicial appointments and the other involving the evolving law of bias in judicial decision-making. In other words, the value we assign identity and experience of Supreme Court justices cannot be disentangled from how we appoint them, or from how we understand judicial impartiality more broadly.In this essay, I elaborate on the questions set out above and focus on the relationship between a representative court, judicial appointments and bias. My analysis is in three parts. In the first part, I explore Canada’s increasing multicultural make-up, the rationales for a Court that reflects the diversity of Canadian society, and the current approach to representation on the Supreme Court of Canada. In the second part, I consider the relationship between a representative court and judicial impartiality, with particular focus on the Supreme Court’s decision in R. v. R.D.S. in 1997. Finally, in the third part, I examine the relationship between representation and judicial appointments.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".