Triage and Dissensus at the Supreme Court of Canada: A Review of the Court’s 2020 Constitutional Decisions
Bibliographic record
Abstract
The onset of the COVID-19 pandemic forced the Supreme Court of Canada to make significant adaptations in 2020. The Court heard fewer appeals, decided fewer cases and adjusted to the necessity of online hearings. Despite the challenges posed by the pandemic, the Court issued a handful of landmark rulings in 2020. These rulings engaged critically with the Court’s past jurisprudence, considered a wide range of scholarship, and broke new ground by boldly clarifying and developing the law. The Court’s 2020 constitutional decisions were also characterized by a dramatic approach to triage and a remarkable degree of dissensus. The Court prioritized its limited jurisprudential resources by deciding a third of the appeals it heard in 2020 in summary oral reasons delivered from the bench. Another troubling feature of the Court’s 2020 opinions is the high level of dissensus they exhibit: the justices were deeply divided on almost all of the major constitutional cases they decided. Dissents are productive and enriching in important ways. But some of the justices’ dissenting energy in 2020 might have been better directed to the writing of reasons — any reasons — in some of the cases that the Court summarily dismissed from the bench.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".