Silencing the crowd: China, the NBA, and leveraging market size to export censorship
Bibliographic record
Abstract
While censorship within China has been a longstanding phenomenon, efforts to suppress information and to reshape perception of China abroad have become increasingly widespread and sophisticated. Recently, this trend has manifested through several high-profile incidents of foreign firms censoring controversial content outside of China in order to retain access to the Chinese consumer market. This article argues that China is uniquely situated to leverage this type of market power due to its enormous, growing consumer base and authoritarian structure. This form of ‘exporting’ censorship can occur in three ways: content bans, position reversal, and self-censorship. Outward-facing firms, especially in the entertainment industry, are particularly vulnerable to this type of pressure as their employees, including actors, athletes, and celebrity CEOs, may have an independent following and audience for their personal views. By analyzing the controversy between China and the National Basketball Association over a single tweet in support of pro-democracy protests in Hong Kong, this article demonstrates the conditions under which censorship efforts may be outsourced to private, foreign actors in jurisdictions outside of China.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".