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Record W2285482189

Courts as Facilitators of Intergovernmental Dialogue: Cooperative Federalism and Judicial Review

2016· article· en· W2285482189 on OpenAlexaffabout
Wade K. Wright

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsJurisdictionFederalismSupreme courtPolitical scienceFacilitatorScrutinyLawSkepticismIncrementalismPoliticsJudicial reviewLaw and economicsConstitutionSociology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.281
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes2
Has abstractyes

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