Dynamic Decentralization in Federations: Comparative Conclusions
Bibliographic record
Abstract
This article presents the conclusions of the project Why Centralization and Decentralization in Federations?, which analyzed dynamic decentralization in Australia, Canada, Germany, India, Switzerland, and the United States over their entire life span. It highlights six main conclusions. First, dynamic decentralization is complex and multidimensional; it cannot be captured by fiscal data alone. Second, while centralization was the dominant trend, Canada is an exception. Third, contrary to some expectations, centralization occurred mainly in the legislative, rather than fiscal, sphere. Fourth, centralization is not only a mid-twentieth century phenomenon; considerable change occurred both before and after. Fifth, variation in centralization across federations appears to be driven by conjunctural causation rather than the net effect of any individual factor. Sixth, institutional properties influence the instruments of dynamic decentralization but do not significantly affect its direction or magnitude. These findings have important conceptual, theoretical, methodological, and empirical implications for the study of federalism.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".