Explaining Dissent on the Supreme Court of Canada
Bibliographic record
Abstract
Abstract.While there is an extensive literature on the causes of dissensus on appellate courts in the US, few empirical studies exist of the causes of dissent in Canadian Supreme Court. The current study seeks to close that gap in the literature, proposing and then testing what we call a Canadian model of dissent. We find that the likelihood of dissent is strongly related to four broad factors that appear to exert independent influence on whether the Court is consensual or divided: political conflict, institutional structure, legal ambiguity in the law and variations in the leadership style of the chief justice. Résumé.Les causes de dissension dans les cours d'appel aux États-Unis font l'objet de nombreux articles et publications, mais il existe très peu d'études empiriques sur les causes de dissidence à la Cour suprême du Canada. La présente étude vise à combler cette lacune en proposant, un modèle canadien de dissension, puis en le mettant à l'épreuve. Nous avons constaté que le risque de dissension est fortement lié à quatre facteurs genéraux qui semblent exercer une influence indépendante, que la Cour soit en accord ou divisée. Ces facteurs sont le conflit politique, la structure institutionnelle, la présence d'une ambiguité juridique dans la loi et le style de direction du juge en chef.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".