Exposing State Repression: Digital Discursive Contention by Chinese Protestors
Bibliographic record
Abstract
One of the major issues in international development is how disadvantaged populations mobilize in response to state repression. Whether in the Black Lives Movement or in the 2011 Arab Spring, digital exposures of police abuse have spurred social movements when people took to social media to expose it. Yet, in authoritarian regimes, citizens cannot easily initiate or participate in social movements. In such cases, how do victims of police violence express their dissatisfaction? This study examines this question in contemporary China, where repression of protesters is well documented. Based on a dataset of microblogs-Chinese tweets-documenting 74,415 protest events in the early Xi administration (2013-2016), this study analyzes how ordinary protestors, including migrant workers, peasants, and the urban poor, expose police abuse in social media. A close reading of microblogs documenting 150 randomly sampled events finds that Chinese protestors adopt three distinct narrative types: citizenship, solidarity, and confrontational. An accompanying quantitative analysis of the wider dataset further finds that ordinary protestors frequently expose police abuse online and that mentions of police abuse are closely associated with the above three narratives. Overall, this study contributes to understanding how abused protestors discursively contest authorities in the world's most powerful authoritarian regime.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".