Federal arbiters as facilitators: Towards an integrated federal and judicial theory for diverse states
Bibliographic record
Abstract
In the majority of federations, the judiciary plays a critical role arbitrating conflict between the orders of government. In general, where this institution is considered within federal and conflict management theory, scholars and policy makers argue the institution should be an independent and neutral body, acting as either a restrained umpire or activist guardian. This article reflects on the foundations of this perspective by looking at the linkages between the main approaches of using federalism to manage diversity and theories of the judicial role. Focusing on the linkages between these two sets of theories illuminates the shared flaw with the traditional focus on independence in the umpire and guardian role-perceptions: the inability to effectively account for the inherently contested nature of diverse federations. To help address this shared flaw, the article sketches an institutional model and theory of judicial review that calls on federal arbiters to recognize the validity of competing viewpoints on the nature of a federation. In adopting this approach, federal arbiters can play a critical role of facilitating ongoing negotiation to manage conflict in diverse states. To help ground the discussion and demonstrate how this more facilitative approach can be implemented in practice, the article briefly highlights how the Supreme Court of Canada is increasingly adopting this role.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".