Opening up mediation opportunities by engaging grassroots data: Adaptive and resilient feminist data activism in China
Bibliographic record
Abstract
This article explores the dynamics and practices of feminist data activism that engages with grassroots data to archive cases of sexual violence in China. Drawing on Cammaert’s notion of the mediation opportunity structure, we investigate the mediation process of a feminist data campaign and activists’ communicative practices in contemporary China. By practicing data-activist research, our study shows that the data-based action repertoire opens up hybrid and contingent mediation opportunities for an anti-sexual violence campaign under the current political opportunity structure. We find the paradox of seeking visibility while refusing mainstream media coverage in activist tactics, which embodies a form of adaptive and resilient feminist data activism in the authoritarian context of China. This case study suggests that the dynamics of feminist data activism in China are configured by the tripartite interaction among the disruptive action repertoire, mediation opportunity structure, and political 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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".