Why do large-scale land protests in China succeed or fail?
Bibliographic record
Abstract
Land protests account for a large portion of all protests in China, but existing scholarship on this topic does not explain under which conditions large-scale land protests succeed or fail. This thesis will attempt to answer this question by focusing on five large-scale land protests that happened in China from 2012 to 2017. I argue that large-scale land protests are more likely to succeed under three conditions: when 1) domestic media reports on this protest are supportive or on the protesters’ side; 2) the protests are violent, and 3) the protests occur in the early stages of a developmental project. Those conditions do not work in isolation, but they coincide with protest success in those five cases that I investigated in this research. Using media analysis and doing a one-and-a-half-month-long period of fieldwork in China, I found that domestic media in China played two roles in determining the outcome of a protest: a “catalyst” role or a “watchdog” role. I also distinguished between short-term outcomes and long-term outcomes and found that the success of a short-term outcome will not necessarily guarantee the long-term outcome of a protest. Thirdly, I found that not only does the level of violence of the protest matter, but also which side used violence first affects the outcome of a large-scale land protest. This research contributed to the literature on contentious politics in China by highlighting under what conditions do large-scale land protests in China tend to succeed in the Xi Jinping era.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".