Contentious Politics in China: Causes, Dynamics, and Consequences
Bibliographic record
Abstract
Abstract China has become a land of social protests. Yet the Chinese state possesses considerable capacity and is rising on the world stage day by day. Why and how do Chinese people take to the streets? Where does their activism lead? This paper draws on a rich body of existing literature to provide an overview of the broad landscape of Chinese contentious politics and to dig deeper into a few common or emerging forms of social conflict. It then explores the various structural and political opportunity-based explanations for why protest occurs in China, before describing the ways in which different organizations and different framings of issues by citizens affect how protests play out. Shifting to where protests lead, the paper briefly surveys a variety of coercive and conciliatory institutions China possesses for social control and then documents distinct patterns in the state’s handling of different types of resistance—repressive, tolerant, concessionary, and mixed approaches—followed by an examination of the multifaceted impact of unrest. The conclusion offers suggestions for future researchers. Reviewing major concepts, debates, perspectives, and emerging research directions in studies of contentious politics in the world’s most populous country, this paper contributes to a more nuanced understanding of authoritarian politics and authoritarian resilience more generally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".