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Record W2197240086 · doi:10.1080/15387216.2015.1095107

Neighborhood conflicts in urban China: from consciousness of property rights to contentious actions

2015· article· en· W2197240086 on OpenAlexaff
Qiang Fu

Bibliographic record

VenueEurasian Geography and Economics · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of British Columbia
FundersLincoln Institute of Land Policy
KeywordsChinaPerceptionConsciousnessContext (archaeology)Government (linguistics)PoliticsProperty rightsProperty (philosophy)Empirical researchPolitical scienceSociologyPsychologyGeographyLawEpistemology

Abstract

fetched live from OpenAlex

Although urban neighborhood conflicts have drawn widespread attention, their possible link with neighborhood perception has not been quantified in the existing literature. Based on a recent neighborhood-based survey of urban residents in Guangzhou, China, this study investigates the structure, determinants, and consequences of neighborhood conflicts. In particular, it finds that there is inherently a subjective dimension embedded in neighborhood conflicts such that these conflicts should be conceptualized and measured as an individual-level perception of neighborhoods. Evidence from both empirical analyses and field research revealed that consciousness of property rights had significant effects on perceived neighborhood conflicts, while both consciousness of property rights and perceived neighborhood conflicts, especially those with local and grass-roots government agencies, further contribute to the occurrence of residents’ contentious actions. By situating neighborhood conflicts in the context of rules consciousness, this study brings attention to neighborhood perception shaping contentious politics in China’s urban transformation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.975

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 science0.0000.000
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 head0.023
GPT teacher head0.253
Teacher spread0.229 · 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.

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

Citations15
Published2015
Admission routes1
Has abstractyes

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