Confiscating progressiveness: the Chinese state’s hegemonic strategies in shaping domestic violence frames on social media
Bibliographic record
Abstract
In the era of social media, authoritarian states are often viewed as barriers to progressive agendas, especially in literature that emphasizes the coercive nature of digital authoritarianism. However, this paper highlights how these states can also use hegemonic strategies to ‘confiscate’ progressive ideas – a process in which a state incorporates progressive civil society discourse and aligns it with its own agenda. To illustrate this process and understand its effects, we conducted a case study on the framing of domestic violence on Chinese social media. This study draws on a dataset we collected of Weibo posts from 2010 to 2019 (N = 616,441). To analyze this dataset, we employed a combination of unsupervised machine learning, qualitative coding, and regression analysis. The analysis revealed four frames of domestic violence: individualist, law-and-order, perception-transformation, and structural. We found that in the short term, the state-endorsed law-and-order frame promoted a progressive agenda on domestic violence on social media. However, the legislation adopting the same law-and-order perspective ultimately undermined another alternative progressive frame, creating more space for the counter-progressive frame. This article contributes to the literature on digital authoritarianism by proposing and articulating the hegemonic measures of state control, which complement the well-documented repressive measures. This reminds researchers and activists of the hegemonic power exerted through state-endorsed progressive programs in authoritarian contexts, which may ultimately undermine political deliberation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".