Two Birds, One Policy: The Establishment of the National Supervisory Commission as a Factional and Centralizing Tool
Bibliographic record
Abstract
Xi Jinping has made “anti-corruption” campaigns a hallmark of his leadership. The campaigns promise to target both “tigers” - senior party, government, and military leaders - and “flies” - local party and government officials. This practice has included a drastic restructuring of China’s anti-corruption and judicial agencies, culminating in their centralization under the National Supervisory Commission (NSC) in 2018. Many scholars have debated whether Xi’s campaigns and the NSC are genuinely intended to combat corruption or are instead a tool to eliminate political opponents and consolidate power. The NSC’s establishment is considered in relation to the two predominant models of anti-corruption drives conducted in China, the “Chongqing” model, and the “Guangzhou” model. By deliberately reproducing the Chongqing model’s accountability defects, eliminating political opponents appears to be a core objective of the NSC’s establishment. However, owing to its centralized nature, the NSC also strengthens the central party’s power over local authorities. Local party branches are far less trusted by the population than their national counterparts. Thus, strengthening the party’s credibility - including a genuine attempt to decrease corruption - and strengthening local government oversight appears to be another objective of the NSC’s establishment.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".