Navigating conflict and fostering co-operation in fiscal federalism
Bibliographic record
Abstract
This paper examines intergovernmental fiscal disputes and co-operation mechanisms across federal and decentralised countries. Employing a case study approach and AI tools, the research analyses constitutional court rulings and their influence on the development of fiscal federalism in seven countries: Australia, Belgium, Brazil, Canada, Germany, India and the United States, with additional insights from Spain, the Netherlands and the European Union. The findings reveal significant variations in the nature and frequency of disputes and judicial interventions, highlighting the crucial role of court decisions in shaping fiscal federalism, most notably in the area of taxation. While conflicts are inherent to decentralised systems, their nature and frequency vary based on each country’s unique constitutional, political, and economic context. The paper recommends strategies for managing disputes and fostering co-operation, including clearly defining powers and responsibilities, enhancing the role of courts in providing fiscal guidance, strengthening intergovernmental institutions and ensuring adaptability to changing conditions. The study concludes that a proactive, collaborative approach involving all tiers of government is crucial to navigate the complexities of fiscal federalism and promote effective governance.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".