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Record W2061925907 · doi:10.1177/0022146510394951

Neighborhood Disadvantage, Network Social Capital, and Depressive Symptoms

2011· article· en· W2061925907 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health and Social Behavior · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSocial capitalConceptualizationDisadvantageInformal social controlSocial network (sociolinguistics)PsychologyInterpersonal tiesSurvey data collectionSocial psychologySocial network analysisDemographic economicsSociologyEconomicsSocial controlPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

Research on why neighborhood disadvantage matters for health focuses on the capacity of neighborhoods to regulate residents' behavior through informal social control. The authors extend this research by conducting a multilevel analysis of data from a 1995 telephone survey of 497 residents of 32 neighborhoods in a U.S. city. The authors find that network social capital mediates the contextual effect of neighborhood disadvantage on depressive symptoms and that health effects of network social capital persist when perceived neighborhood disorder, a standard indicator of low informal social control, is controlled for. The findings demonstrate the value of a conceptualization and measurement of network social capital that (1) considers ties that transcend neighborhood boundaries, (2) investigates health benefits of network social capital in the forms of closure and embedded support resources and range and embedded instrumental resources, and (3) uses network data on specific network members with strong and weak ties to respondents.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.048
GPT teacher head0.362
Teacher spread0.314 · 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