Bibliographic record
Abstract
Abstract Social unrest is on the rise in China. Few incidents of public demonstrations, disruptive action or riots occurred in the 1980s, but the 1989 student protests in Tiananmen Square marked a turning point. In 1993, there were already 8, 700 ‘mass incidents’ recorded. By 2005, the number had grown tenfold to 87, 000. Unofficial data estimated by a researcher at Tsinghua University suggests that there were 180, 000 incidents in 2010.1 These figures could easily be interpreted as signs that the days of the Chinese Communist Party’s (CCP) rule are numbered. However, the number of media outlets has proliferated since the 1990s; and with that, the incentive to report on eye-catching stories has increased. In comparing these incidents with the protests that toppled several authoritarian regimes during the Arab Spring of 2011, a number of significant differences emerge. The scale of most protests in China is much smaller. Protestors are usually a homogenous group, such as peasants, taxi drivers, migrant workers or homeowners. Mobilisation across social groups, an important precondition for system-threatening collective action, is therefore largely absent. Further, despite rising unrest, the death toll in such activities remains low.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".