Political Resources and Divergent Court Empowerment in China: A Subnational Comparison
Bibliographic record
Abstract
The cities of Guiyang and Kunming are known among legal scholars, practitioners, and policy makers for hosting two of China’s earliest specialized environmental tribunals, following serious water contamination in the two cities. However, the judicialization of environmental protection appears to be relatively nominal in Kunming and substantial in Guiyang. Why? We contend that, at a critical juncture, different political resources available to local leaders—including their past networks and experiences—led them to implement different strategies to deal with these crises. Under similar conditions, different political resources thus led to divergent outcomes of judicial empowerment. We use process tracing to describe the causal sequence in the adoption and application of policies of judicialization. Whether courts are empowered to operate proactively or conservatively is the result of the strategies of local actors in response to the policy agenda set forth by political leaders and constrained by political leaders’ available political resources. This study contributes to existing theories of court empowerment in authoritarian regimes that have largely relied on national-level or socioeconomic factors. Through a controlled subnational comparison in China, this article provides an alternative theory of divergent practices of court empowerment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".