‘Saving or Keeping Face Was Just Part of the Game’: The Role of Facework in White-Collar Crime in Chinese Football
Bibliographic record
Abstract
Abstract This article introduces the concept of ‘facework corruption’ as a theoretical framework for understanding white-collar crime in Chinese football. Drawing from an analysis of 15 judicial cases and 25 semi-structured interviews with football officials, coaches, and corruption investigators, the study identifies three key mechanisms through which facework facilitates corruption and match-fixing. First, ‘Face as currency’ explores how financial incentives and face are deeply intertwined, with bribes and illicit transactions serving to enhance status and reinforce power networks. Second, ‘national face as justification’ highlights how corrupt individuals rationalize their actions as contributing to China’s ambition to become a football superpower. Third, ‘facework networks’ demonstrate how interpersonal and institutional ties normalize corruption, embedding it within football governance structures. These mechanisms illustrate that corruption in Chinese football is not merely a financial crime but also a performative social act, where face serves as both a motivation and a justification for wrongdoing. The findings contribute to criminological literature by integrating the facework framework with existing theories of white-collar crime, demonstrating its broader applicability beyond sport.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".