Intersectional digital feminism: assessing the participation politics and impact of the MeToo movement in China
Bibliographic record
Abstract
Feminist media scholarship has been keen on explicating the ways that digital media have shaped feminist politics in recent decades. Through analyzing the MeToo movement in China, this study contributes to a further understanding of the role of digital media in facilitating feminist activism and movements. We propose a framework of intersectional digital feminism that embraces the perspectives of inclusion/exclusion, visibility/invisibility, and impact/backlash to assess feminist protests and actions in the digital age. The framework also calls for a contextual analysis that accounts for political, social-cultural, and historical circumstances. Drawing upon textual analysis of public and media discourses about China’s MeToo movement, the study finds that the formation of the movement in China was attributed to the online and offline formation of feminist, subaltern, and pro-change counter-publics; the backlash came mainly from censorship and misogynistic attacks; and rural and working-class women are largely marginalized and underrepresented in China’s present feminist movement. We argue that MeToo manifests both the potential to change gender hierarchies in the digital age and the limitation that structural inequalities cannot be changed by technologies per se. Feminist activism should develop more inclusive agendas and mobilizing strategies pertinent to specific contexts.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".