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Record W2601725537 · doi:10.1177/2158244017700462

Power, Apathy, and Failure of Participation

2017· article· en· W2601725537 on OpenAlexaff
Sibo Chen

Bibliographic record

VenueSAGE Open · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsContext (archaeology)NeglectCitizen journalismChinaTourismEmpowermentApathySociologyPolitical sciencePublic relationsEconomic growthGeographyPsychologyLaw

Abstract

fetched live from OpenAlex

Public participation is widely regarded as a vital component for making environmental decisions more democratic, legitimate, and effective. Yet, research on this subject has largely focused on rights and principles instead of context and process, especially in non-Western settings. To address this gap, this article explores how local voices on environmental issues were muted in a Chinese rural context. It describes controversies surrounding a cultural and ecological tourism development in Heyang, a transforming village in the east coastal region of China. Based on semistructured group interviews, the article reveals that although many issues found in the Heyang case resonated with similar cases in Western settings, such as the lack of access to information and the problematic solicitation of public input, fundamentally, the local voices were muted by the village council’s blind adoption of an urban-centric ecological modernization agenda and its neglect of local villagers’ emotional attachment to their land properties. The above findings not only draw our attention to how participatory communication can be compromised by contextual factors but also invite us to reconsider how China’s existing urban–rural division fundamentally influences its ecological civilization.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.309
Teacher spread0.296 · 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 designObservational
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

Citations7
Published2017
Admission routes1
Has abstractyes

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