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Record W1618446380 · doi:10.1002/jcop.21528

PARENT INVOLVEMENT IN EDUCATION AS A MODERATOR OF FAMILY AND NEIGHBORHOOD SOCIOECONOMIC CONTEXT ON SCHOOL READINESS AMONG YOUNG CHILDREN

2013· article· en· W1618446380 on OpenAlexaff
Sharon Kingston, Keng‐Yen Huang, Esther J. Calzada, Spring Dawson‐McClure, Laurie Miller Brotman

Bibliographic record

VenueJournal of Community Psychology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsSocioeconomic statusModerationPsychologyDevelopmental psychologyMultilevel modelContext (archaeology)Social environmentAcademic achievementDemographySocial psychologyPopulationGeographySociology

Abstract

fetched live from OpenAlex

Limited socioeconomic family and neighborhood resources are known to influence multiple aspects of school readiness skills. Early parent involvement in education is hypothesized to attenuate risk for academic underachievement related to socioeconomic disadvantage. The current study used multilevel modeling to test whether parent involvement moderates the effects of family and neighborhood level socioeconomic resources on school readiness among a sample of 171 urban 4‐year‐olds. Parent involvement moderated the effect of family and neighborhood socioeconomic resources on the social‐emotional‐behavioral components of school readiness. Increased parent involvement in education was related to lower rates of behavior problems among children of single parents and among children from neighborhoods with higher levels of childcare burden. In contrast, parent involvement did not moderate the relation between socioeconomic risk and cognitive‐academic components of school readiness skills.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.383
Teacher spread0.329 · 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

Citations42
Published2013
Admission routes1
Has abstractyes

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