Bibliographic record
Abstract
Most scholars credit policy experimentation with China’s successful economic reforms and continued authoritarian resilience. This article contributes to this policy experimentation literature by providing a systematic overview of the motivations incentivizing Chinese policy entrepreneurs to experiment at local levels. This article traces the evolution of policy experimentation in China through the prism of understanding the varying motivations for experimentation, such as individual career incentives, improving good governance, and symbolic and factional politics. Despite the benefits of policy experimentation, there has been a notable reduction in experimentation in the Xi Jinping era due to the recentralization of political power through “top-level design” and an ongoing anti-corruption campaign. This has effectively created disincentives to innovate at the local level. Nevertheless, we do find remaining pockets of policy experimentation that we argue are due to ineffective institutional incentives, the influence of peer groups, and variations in the individual personalities of policymakers. However, it is unclear if the remaining experimentation is robust enough to assist in further economic reforms or adaptive 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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".