Why is Bribing Doctors an Excusable Crime? The Normalization of Professional Corruption
Bibliographic record
Abstract
Abstract Professional corruption is a pervasive issue with significant societal consequences. This study investigates the normalization of professional corruption by examining bribery among physicians in Chinese hospitals, where informal payments, or ‘red packets’, from patients were often reported despite being illegal. Drawing on a qualitative case study, we develop a process model comprising four mutually reinforcing building blocks: organizational arrangements, which capture distinct features of professional campuses that enable corruption; constrained supervision, which weakens institutional governance and limits accountability to clients; rationalizations, through which professionals justify informal payments by emphasizing professional expertise and social respect while downplaying the social trusteeship value; and socialization, which operates through a combination of structural assurance and backstage cocoons that desensitize professionals to the ethical implications of bribery. Furthermore, we highlight the role of cultural resonance in linking corrupt practices to traditional gift‐giving customs, thereby legitimating informal payments in professional contexts. Our findings contribute to understanding how organizational factors and cultural traditions jointly enable corruption, offering insights applicable to other professional organizations while emphasizing important boundary conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".