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Record W4412996098 · doi:10.1002/ajcp.70006

Social connections to neighbors and NIMBYism among public housing residents in Seoul

2025· article· en· W4412996098 on OpenAlexaff
Gum‐Ryeong Park, Jinho Kim

Bibliographic record

VenueAmerican Journal of Community Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsHamilton Health SciencesMcMaster UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsNIMBYPublic housingRentingHealth psychologyDemographic economicsSurvey data collectionRental housingInterpersonal tiesPublic healthSocioeconomicsPanel dataEconomic growthSocial psychologyPublic economicsSociologyPsychologyPolitical scienceEconomicsMedicine

Abstract

fetched live from OpenAlex

The study examines whether and how transitions into and out of social connections with neighbors have asymmetric effects on residents' attitudes toward the siting of locally unwanted land uses-commonly referred to as "Not In My Backyard" (NIMBY) responses. These facilities, like special schools or public housing, may benefit society, but are often opposed locally due to perceived harms. We used data from the Seoul Public Rental Housing Panel Survey (N = 6317). An asymmetric fixed effects model was employed to separately estimate the associations for transitioning into and out of social connections to neighbors. Additionally, gender-stratified models were used to examine whether the asymmetric effects of these transitions differ by gender. Transitioning out of social connection to neighbors is associated with an increase in NIMBYism (b = 0.149), which was larger than the decrease observed when transitioning into social connections to neighbors (b = -0.064). These effects were more pronounced for women than for men. Policymakers should consider initiatives that strengthen community bonds as a strategy to reduce NIMBYism and promote inclusive neighborhood planning.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.418
Teacher spread0.356 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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