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Record W2037494784 · doi:10.1007/s10464-005-9001-8

Neighborhood Poverty, Social Capital, and the Cognitive Development of African American Preschoolers

2006· article· en· W2037494784 on OpenAlexaff
Margaret O’Brien Caughy, Patricia O’Campo

Bibliographic record

VenueAmerican Journal of Community Psychology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsPovertySocial capitalHealth psychologyPsychologySocial environmentContext (archaeology)Developmental psychologySocioeconomic statusChild developmentCognitionSocioeconomicsPublic healthSociologyEconomic growthGeographyDemographyEconomicsMedicinePopulationSocial science

Abstract

fetched live from OpenAlex

In this investigation, we examine the impact of the ecological context of the residential neighborhood on the cognitive development of children by considering social processes not only at the family-level but also at the neighborhood-level. In a socioeconomically diverse sample of 200 African American children living in 39 neighborhoods in Baltimore, we found that neighborhood poverty was associated with poorer problem-solving skills over and above the influence of family economic resources and level of positive parent involvement. Sampson has theorized that neighborhood poverty affects child well-being by altering levels of neighborhood social capital as well as family social capital. Although we found that indicators of neighborhood and family social capital were associated with cognitive skills, these factors did not explain the association between neighborhood poverty and problem-solving ability. Implications for future research in the area of neighborhoods and child development are discussed.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.338
Teacher spread0.310 · 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 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

Citations98
Published2006
Admission routes1
Has abstractyes

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