Justice for Whom? Redressing the “1975 Shadian Incident” in the Post-Mao Era, 1978–2019
Bibliographic record
Abstract
The “Shadian conflict,” which erupted in 1964 and continued until at least 1975, was the largest religious resistance of the Cultural Revolution, but its local dynamics and sociopolitical impacts are significantly understudied. This article sheds light on how Chinese Communist Party (CCP) authorities have dealt with Shadian Muslims’ petitions and requests for religious freedom from 1979 until 2019. It argues that the post–Mao Zedong CCP leadership has continued to implement the same mentality and methods as in the Mao period to deal with ethno-religious conflicts. At the center of this process lies the events of 1975 that have come to be known as the “Shadian incident” or “Shadian massacre,” in which around 1,600 Shadian Muslims were killed. The party’s approaches to redressing the events of 1975 have secularized and simplified the causes of the Shadian massacre and the religious requests of Muslim villagers by attributing the tragedy and villagers’ protests to factional struggles launched by “followers of Lin Biao and the Gang of Four” and “a handful of chaos-making figures.” Embedded in ongoing struggles between vernacular and official narratives of the 1975 tragedy, the Shadian problem has resulted in unreconciled discord between the CCP, which prioritizes the Maoist class-struggle mentality, and villagers, who emphasize Islamic religiosity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".