Criminalising feminism: Feminist activists’ experiences and perceptions of state repression in China
Bibliographic record
Abstract
State repression of women remains a pressing issue worldwide, yet it is seldom classified as a form of crime in criminological research. While feminist criminology and victimology have extensively explored the experiences of women victims within the context of violence against women, empirical research into women's perceptions and experiences of state repression remains limited in criminology. This article aims to broaden the scope of inquiry to encompass a wider array of victim experiences. This study applies the lens of political repression and state crime victimisation to the situation of feminist activists facing state repression in China, delving into the tactics, characteristics and repercussions of such repression. By conducting detailed interviews with feminist activists and analysing material from public sources, our data illustrates that state repression of feminist activists is perceived by victim-survivors as a form of state crime that harms women both physically and mentally and facilitates other forms of crime against women in society. We argue that victim-survivors’ experiences and perceptions underscore the critical importance of prioritising the act of listening to their voices in criminological discussions of state misconduct.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".