Great-Power Decentralization and the Management of Global/Local Economic Policy and Relations: Lessons in Fluidity from the People's Republic of China
Bibliographic record
Abstract
This article compares the practice of great power federalism in terms of global/local economic policy and relations in a context of expanding regional influence and transcontinental reach. The authors contrast China's recent decentralization experience with that of the FRG and the United States with respect to management of productive ventures, regulation of the economy, trade and commerce, fiscal relations, monetary policy and labor mobility. The three great-power states manage their complex and far-reaching economies through systems of multi-level governance that exhibit elements of convergence at the same time as each is headed in a defining direction. German federalism is making room for supranational involvement, US federalism emphasizes new managerialism at all levels of government and China's post Mao de facto federalism is launching provincial and sub-provincial governments on a booming economic trajectory. China's recent performance is particularly impressive given the size of its population and the extent to which its economy has been transformed and energized. In key respects, China's administrative system is the most decentralized and fluid. The effective participation of Chinese sub-national entities in transterritorial economic undertakings is particularly striking. China's experience suggests that the requisite energy and capacity to tackle trans-national economic challenges might lie at the sub-national level. Given the shifting nature of global pressures and local priorities, the extra-organizational sensitivities and linkages of sub-national public managers must include proximate and distant economic conditions, central government overseers and trans-national actors. In this dynamic context, the most fluid forms of federalism are likely to have an edge.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".