From “Big White” army to White Paper Protests: China’s gendered pandemic war and feminist interventions
Bibliographic record
Abstract
In response to the COVID-19 pandemic, many nation-states adopted militarized measures for rapid containment. China notably implemented extreme and enduring war-like procedures. This study aims to explore the implications of securitizing the pandemic through a feminist lens and suggest ethical and equitable strategies for understanding emergencies and coping with future crises. Employing discourse-based online ethnography, it analyzes social dynamics in China’s “pandemic war,” examining how gender and (in)security are co-constructed and the role of feminist activism within an authoritarian context. This article argues that framing the pandemic as war reinforces a masculinist stance of dominance, prioritizing state stability over human rights and security. State-centric securitization perpetuates state authority, gendered social hierarchies, and structural violence. The exclusionary nature of this approach created new avenues for feminist activism, both online and offline, advocating for marginalized groups, raising social and gender awareness, challenging state narratives, and fostering collective resistance. Drawing lessons from Chinese feminist initiatives during COVID-19, this study calls for alternative crisis responses focused on care, equity, ethics, and collaboration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".