Innovation and Preservation: Remaking China's National Leadership Training System
Bibliographic record
Abstract
Abstract This article details the reorganization of China's national leadership training system, and analyses the reforms as an integral element of the Chinese Communist Party's efforts to adapt institutionally to a rapidly changing environment. Three main findings are presented. First, the national leadership training system is being remade under the direction of the Party's Central Organization Department to give greater emphasis to the “spirit of reform and innovation,” as seen especially in the creation of the China Executive Leadership Academy in Pudong, Shanghai, and in the formation of sister academies in Jinggangshan and Yan'an. Second, China's political elite have given greater priority to leadership innovation, although they are trying to balance this with ensuring that sufficient attention and resources are also given to preserving the ruling status of the CCP. Third, by establishing the new group of training academies under the COD, the Party is diversifying beyond the Party School system for leadership research and training. The article suggests that the guiding logic behind these reforms is to promote enough innovation in managerial training and research to enable the Party to meet the changing governance requirements of the market transition and economic globalization, while at the same time putting in place institutional measures that help to preserve the Party's rule.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".