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Voices of Protest Against Industrial Pollution in Hubei, China, During the 1970s and 1980s

2020· article· en· W3035457894 on OpenAlexaff
Liu Yun

Bibliographic record

VenueEnvironment and History · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsQueen's UniversityUniversity of Regina
Fundersnot available
KeywordsChinaPollutionNegotiationPolitical scienceCorporate governanceIndustrial pollutionAir pollutionEnvironmental protectionGeographyBusinessLaw

Abstract

fetched live from OpenAlex

Abstract This article examines local official records to find voices of protest against industrial pollution in Hubei, China, during its early reform era from the 1970s to the 1980s. Archival evidence from unpublished official documents indicates that to some extent local officers responded to citizens' petitions against two main forms of industrial pollution: air pollution and soil pollution. Air pollution mostly affected urban residents but elicited more contention. Soil pollution got comparatively less exposure but caused more direct damage to impacted peasants. Both rural and urban victims of industrial pollution projected their own voices of protest typically by submitting group-authored and signed or anonymous whistle-blowing letters. Protests against pollution emerged with inter-group conflict negotiation in public or semi-public venues as well as in local investigation reports. The findings discussed here help to explain how local environmental governance evolved through increasing public awareness at subnational levels in China's early reform years.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.016
GPT teacher head0.191
Teacher spread0.175 · 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 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
Published2020
Admission routes1
Has abstractyes

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