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Record W4406437291 · doi:10.1086/733417

Why Was Small Not Beautiful? Rethinking China’s Great Leap Forward through Water

2025· article· en· W4406437291 on OpenAlexaff
Mengran Xu

Bibliographic record

VenueEnvironmental History · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChinaHistoryGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Utilizing both ministerial records at the national level and local archival documents from Lankao County, Henan Province, this article examines how small-scale water projects turned into environmental disasters during the Great Leap Forward (1958–62). Previous scholarship has often regarded the Great Leap water policy, namely, the Three Priorities (water storage, small scale, and mass based), as a testimony to the irrationality of Maoist mass mobilization. This article, by contrast, interprets the Maoist technological complex as a troubled marriage between two variants of developmentalism: the high-modernist pursuit of productivism and scientific rationality (water storage) and the Maoist faith in decentralized mass initiatives (small-scale, situated, local projects). The article argues that Maoist water politics were undone by their internal contradictions, because high-modernist centralism undermined populist, revolutionary mass activism—a conflict that extended beyond hydrological engineering and weakened Mao’s revolution as a whole.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.222
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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