Interventions at the Supreme Court of Canada: Accuracy, Affiliation, and Acceptance
Bibliographic record
Abstract
Interveners make submissions in about half of the cases heard by the Supreme Court of Canada, but the motivations for and consequences of the practice are not clearly understood. Considered broadly, there are at least three functions that the practice of intervention might perform. The first possibility is that 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 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). We examine empirically the role of interveners in all the cases decided by the Supreme Court of Canada from January 2000 to July 2009 and find statistical evidence that interveners matter.
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 | 0.019 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".