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Record W1562198670 · doi:10.22439/cjas.v26i1.1372

Burning the Grassroots: Chen Boda and the Four Cleanups in Suburban Tianjin

2008· article· en· W1562198670 on OpenAlexaff
Jeremy Brown

Bibliographic record

VenueThe Copenhagen Journal of Asian Studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsSimon Fraser University
FundersAndrew W. Mellon Foundation
KeywordsGrassrootsMemoirChinaChenPower (physics)Working classHistoryConfusionSociologyEconomic historyPolitical sciencePoliticsLawPsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.314
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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