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Neighborhood Matters: Racial Socialization of African American Children

2006· article· en· W2026301265 on OpenAlexaff
Margaret O’Brien Caughy, Saundra Murray Nettles, Patricia O’Campo, Kimberly Fraleigh Lohrfink

Bibliographic record

VenueChild Development · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologySocializationDevelopmental psychologyAssociation (psychology)Social capitalPromotion (chess)Cognitive developmentCognitionSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Differences in racial socialization practices and their effects were examined in a sample of 241 African American 1st graders (average age 6.59 years) living in an urban area. Child outcomes included cognitive development, receptive language skills, and child problem behavior. The cultural environment of the home was associated with higher cognitive scores for boys living in high negative social climate and low social capital neighborhoods and for girls living in high social capital neighborhoods. The positive association of promotion of mistrust and child behavior problems was magnified in neighborhoods that had low levels of social capital. A high negative social climate in the neighborhood attenuated the positive association between preparation for bias/promotion of mistrust and externalizing problems.

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)0.002
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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.007
GPT teacher head0.256
Teacher spread0.250 · 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

Citations198
Published2006
Admission routes1
Has abstractyes

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