Interventions at the Supreme Court of Canada: Accuracy, Affiliation, and Acceptance
Bibliographic record
Abstract
Do interveners matter? Under Chief Justice McLachlin the Supreme Court of Canada has allowed an average of 176 interventions per calendar year and interveners have cumulatively made submissions in half of the cases heard by the Court. This level of activity suggests that interveners are doing something. But what is it that they are doing? \n \nIn the abstract, there are at least three functions that the practice of intervention might perform. First, hearing from interveners might provide objectively useful information to the Court (i.e., interveners might promote the “accuracy” of the Court’s decision-making). A second possibility is that the practice of intervention allows interveners to provide the “best argument” for certain partisan interests that judges might want to “affiliate” with. A third possibility is that interventions are allowed mainly (if not only) so that intervening parties feel they have had their voices heard by the Court and by the greater public, including Parliament, regardless of the effect on the outcome of the appeal (i.e., the Court might be promoting the “acceptability” of its decisions by allowing for an outlet for expression). \n \nIt is disconcerting that until now the effects of interventions on the decision-making of the Supreme Court of Canada have not been systematically explored through empirical analysis. A growing body of literature has examined the role of amicus curiae at the Supreme Court of the United States. To date, however, the related literature in Canada is slim and, to the extent it exists, does not deploy the empirical methods necessary to test independently for the influence of interveners on the decisions of individual judges. This work fills this gap in the existing literature and expands our collective understanding of the consequences of the practice of intervention at Canada’s highest court. We find evidence that interveners matter more than many observers might expect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".