How Should Courts Respond to Political Questions? Exploring the Dialogical Turn in the Supreme Court of Canada’s Federalism and Indigenous Case Law
Bibliographic record
Abstract
In this article, we: (1) advance a theory for how courts should respond to highly political disputes about jurisdictional authority, and (2) assess whether courts can achieve this ideal. Our theory draws from normative realism to argue that courts should push conflict back into the political realm whenever possible—facilitating free and fair dialogue by outlining rules and principles to guide negotiations, while also rejecting zero-sum outcomes when enforcing jurisdictional powers and related rights. We favor this approach because it can generate legitimacy for the legal and political systems by recognizing the judiciary’s limited democratic standing in structural disputes. To ground this argument in actual practice, we assess how the Supreme Court of Canada has managed two streams of highly political jurisprudence related to jurisdictional authority—federalism and Aboriginal rights cases. We show that the Court has increasingly relied on this approach of facilitating dialogue in both areas. While we argue that this approach is particularly well suited to federalism cases, our analysis uncovers negative outcomes in Indigenous case law. The Court’s approach often fails to strongly enforce the constitutional rights of Indigenous peoples, demonstrating that its facilitator role does not adequality account for the power imbalances between the state and Indigenous peoples.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.045 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".