The Supreme Court, Functional Bilingualism, and the Indigenous Candidate: Reconciling the Bench
Bibliographic record
Abstract
The recent reforms to the Supreme Court of Canada appointment process have created potential barriers to Indigenous candidates. This article reviews the appointment process and its two objectives of functional bilingualism and increased diversity. Given the lack of progress on diversity, particularly with respect to Indigenous representation, a rebalancing of appointment criteria is required. Reconciliation, in both the legal and evolving public policy sense, requires Indigenous participation in legal institutions. Furthermore, arguments in favour of functional bilingualism, such as specific legal expertise and incorporation of distinct cultural viewpoints, transfer seamlessly to Indigenous and other minority representation at the Supreme Court. The functional bilingualism requirement should remain, but accommodation for Indigenous candidates is integral to reconciliation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".