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Record W2214467156 · doi:10.12735/ier.v3i3p16

Social and Behavioral Problems in an Urban At-Risk Preschool Population

2015· article· en· W2214467156 on OpenAlexvenueno aff
Nathan Missen, Stephen J. Bagnato, Daniel S. Wells, Laura M. Crothers, Ara J. Schmitt, Jered B. Kolbert

Bibliographic record

VenueInternational Education Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPopulationSocial riskDevelopmental psychologyEnvironmental healthSociologyDemographyMedicine

Abstract

fetched live from OpenAlex

Early identification of children who display elevated rates of interpersonal and behavioral problems is vital for the initiation of early intervention services. Teaching students social-emotional skills is an important goal of preschool programs, including Head Start programs, across the United States. In order to better understand the rates of interpersonal and behavioral delays demonstrated by preschool students participating in an urban Head Start program, as well as any demographic-based risk factors that may predict these problems, 1,399 (86 % Black/African American) students were administered the Preschool and Kindergarten Behaviors Scales – 2nd edition (PKBS-2). Results indicate that gender is significantly associated with both social and behavioral challenges. Specifically, in comparison to girls, boys tend to be less socially adept and more likely to display troublesome behaviors. Suggestions for future research, such as longitudinal studies, are included.

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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.219
GPT teacher head0.530
Teacher spread0.311 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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