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Record W2165637547 · doi:10.1002/bsl.2035

Gender Differences in Physical Aggression and Associated Developmental Correlates in a Sample of Canadian Preschoolers

2012· article· en· W2165637547 on OpenAlexaffabout
Patrick Lussier, Raymond R. Corrado, Stacy Tzoumakis

Bibliographic record

VenueBehavioral Sciences & the Law · 2012
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsAggressionPsychosocialPsychologyDevelopmental psychologyPoison controlInjury preventionEarly childhoodLatent class modelLongitudinal studyHuman factors and ergonomicsDemographyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Recent studies have indicated that gender differences in children's aggressive behavior emerge during the preschool years and that these differences are relatively stable during childhood. The current study assesses whether these gender differences can be observed when a multidimensional measure of aggression from the ongoing Vancouver Longitudinal Study on the psychosocial development of children is utilized. Specifically, the level of physical aggression (PA) in three cohorts of children (aged three, four, and five years) from the initial 338 families in the Wave I data recruited for this study was analyzed using a series of constrained and unconstrained latent class models. Three latent classes of physically aggressive children were identified (low, moderate, and high level), with boys being over-represented in the highly aggressive group and being five times more likely than girls to show high levels of aggression. No age effects were detected, suggesting gender differences from the age of three years onward. The correlates of PA were similar for both boys and girls. Particularly important, a small subgroup of highly aggressive boys emerged from the study showing a clinical profile similar to Moffitt's life-course-persistent antisocial pattern. Such a group was not identified for girls.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.063
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

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

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 head0.084
GPT teacher head0.328
Teacher spread0.244 · 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 teacher head, 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

Citations22
Published2012
Admission routes2
Has abstractyes

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