Burning the Grassroots: Chen Boda and the Four Cleanups in Suburban Tianjin
Bibliographic record
Abstract
Abstract Chen Boda's four cleanups model in Xiaozhan, a marshy area southeast of Tianjin, was as important as Wang Guangmei's Taoyuan brigade in 1964, but is less well known. As Chairman Mao's top theorist and the editor of Red Flag, Chen Boda enjoyed support from Mao and Liu Shaoqi as he uncovered evidence of 'revisionism' in Tianjin's south suburbs. Chen's claims led to a witch-hunt that killed tens of people and tortured and imprisoned many others. Beyond Tianjin, the 'Xiaozhan experience' was promoted as a successful 'power seizure' in a central document circulated nationwide in October 1964. The document pushed the socialist education movement in a more radical direction, causing the downfall of rural cadres across China. This article draws upon archival sources, memoirs, and interviews to detail Chen Boda's contentious interactions with Tianjin officials and suburban villagers. Chen allowed the four cleanups to turn brutal in Xiaozhan, and his vision of a rural China dominated by class enemies differed from what work team members experienced. Ironically, in order to keep the Xiaozhan experience from falling apart, Chen had to resort to methods similar to those used by the village cadres he had recently overthrown. Chen Boda's meddling in Xiaozhan reveals considerable diversity—and indeed, confusion—in how top leaders interpreted and carried out Mao's shifting plans for the countryside during the four cleanups. This confusion led to disastrous outcomes for rural residents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".