Courts as Facilitators of Intergovernmental Dialogue: Cooperative Federalism and Judicial Review
Bibliographic record
Abstract
The courts in Canada have often been cast, by both courts and legal scholars, as “umpires” or “arbiters” of the division of powers – umpires or arbiters that have the exclusive, or at least primary and decisive, authority to clarify, enforce, and resolve disputes about the allocation of jurisdiction in the federal system. This article critically examines a novel alternative role for the courts, a role that is evident, I argue, in the Supreme Court of Canada’s recent division of powers decisions. In this role, the courts are cast as facilitators of “cooperative federalism”, or what I call intergovernmental dialogue – allocations of jurisdiction worked out, directly or indirectly, by the political branches in the intergovernmental arena, not by the courts. As facilitator, the Court limits its role in imposing particular substantive outcomes, and attempts to encourage, accommodate and reward intergovernmental dialogue, in part by deferring to it where it occurs. The article explores the arguments that might be thought to weigh in favour of this facilitative role, attempting, in the process, to shed some light upon what may account for the Court’s attraction to it. It argues that these arguments do not hold up, well or at all, when subjected to closer critical scrutiny, and that there are various reasons to be sceptical of an approach that casts the courts as facilitators of intergovernmental dialogue.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| 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".