Rice Bunnies vs. the River Crab: China’s Feminists, #MeToo, and Networked Authoritarianism
Bibliographic record
Abstract
As censorship algorithms for digital communications evolve in China, so do netizens’ evasion techniques. In the last two decades, strategic users have employed the language of satire to slip sensitive content past censors in the form of euphemisms or analogies, with messages ranging from lighthearted frustration to wide scale resistance against repressive government policies. In recent years activists have used spoofs to discuss controversial subjects, including the president, violent arrests by the Domestic Security Department, and even the #MeToo movement. In addition to providing an outlet for criticism and free speech, spoofs can also be a powerful organizational tool for activists in authoritarian societies through their ability to facilitate decentralized, personalized, and flexible connective action. This paper investigates how feminists used spoofs for social mobilization throughout China’s #MeToo movement while evaluating potential frameworks for measuring activists’ success against the media censorship and political repression of a networked authoritarian regime.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".