Residual Corruption in the Chinese Civil Service: Towards an Ecosystem Theory
Bibliographic record
Abstract
Our understanding of corrupt behavior and of corruption perpetuation at organizational and societal levels is fairly stable and consensual, largely due to normative approaches taken around it in both research and practice. Arguably, knowledge has reached a critical point where we ought to consider more nuanced facets of corruption , to enable future fecundity in this area. One way forward is examining rich contextual data to extract strikingly different perspectives on corruption. Our focus is the Chinese civil service: a highly guarded context with a distinct cultural and political identity. Specific concepts such as Guanxi, Confucianism and political capitalism can afford us an insight into China’s unique political arena (Li-Chia 2021), revealing new perspectives on corruption. Our interview data from 31 high-level Chinese civil servants suggests that individual corruption with which individuals engage to fit in, rather than move ahead, of their collective is seen to contribute to a type of corruption we call ‘residual corruption’ which confers some equilibrium in a corruption ecosystem where there are drivers for corruption, but also breaks, checks and balances. These findings challenge our current understanding of corruption normalization which suggests uncontrollable growth in the absence of external control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".