Why Does the Federal Government Appeal to the Supreme Court of Canada in Charter of Rights Cases? A Strategic Explanation
Bibliographic record
Abstract
Despite the impressive body of scholarship dedicated to analyzing litigation involving the Charter of Rights and Freedoms in the Supreme Court of Canada, there remains an incomplete understanding of why these cases come to the Court. Notably absent from the literature is sustained analysis of why governments, the most frequent class of appellant, bring Charter cases to the Supreme Court. Recent work has addressed the decision to appeal by the U.S. federal government and state attorneys general and provides an excellent theoretical starting point. I use case data collected from interviews with federal government lawyers and law reports to test whether the Canadian federal government's decisions to appeal to the Supreme Court of Canada in Charter cases are also “procedurally rational.” I conclude that these decisions are primarily shaped by strategic considerations related to policy costs, case importance, reviewability, and the prospect of winning on appeal, regardless of the party in power. In the process, the article further extends the application of strategic decisionmaking theory with regard to law and courts beyond judicial behavior, and beyond the U.S. context.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".