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Record W2113104287 · doi:10.1017/s0954579410000295

Aggression, social competence, and academic achievement in Chinese children: A 5-year longitudinal study

2010· article· en· W2113104287 on OpenAlexaff
Xinyin Chen, Xiaorui Huang, Lei Chang, Li Wang, Dan Li

Bibliographic record

VenueDevelopment and Psychopathology · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyAggressionDevelopmental psychologyAcademic achievementSocial competenceLongitudinal studyCompetence (human resources)Clinical psychologySocial changeSocial psychology

Abstract

fetched live from OpenAlex

The primary purpose of this longitudinal study was to examine, in a sample of Chinese children (initial M age = 8 years, N = 1,140), contributions of aggression to the development of social competence and academic achievement. Five waves of panel data on aggression and social and school performance were collected from peer evaluations, teacher ratings, and school records in Grades 2 to 5. Structural equation modeling revealed that aggression had unique effects on later social competence and academic achievement after their stabilities were controlled, particularly in the junior grades. Aggression also had significant indirect effects on social and academic outcomes through multiple pathways. Social competence and academic achievement contributed to the development of each other, but not aggression. The results indicate cascade effects of aggression in Chinese children from a developmental perspective.

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)0.001
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 score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.324
Teacher spread0.300 · 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

Citations162
Published2010
Admission routes1
Has abstractyes

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