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Record W2134034261 · doi:10.5539/jedp.v3n1p234

Developmental Trajectories of Oppositional Behavior during Elementary School and Their Risk Factors

2013· article· en· W2134034261 on OpenAlexaffvenue
Marc Tremblay, Stéphane Duchesne, Frank Vitaro, Richard E. Tremblay

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité de MontréalMontfort HospitalUniversité Laval
Fundersnot available
KeywordsOpposition (politics)AggressionPsychological interventionPsychologyDevelopmental psychologyPopulationAnxietyClinical psychologyDemographyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Oppositional and defiant behavior (ODB) problems are among the most important behavior problems in school children. Understanding their trajectories during elementary school and conditional risk factors at school entry is essential for implementing effective preventive and corrective interventions. Behavior problems of a population sample (958 boys, 971 girls) attending public schools were assessed annually by teachers. Three groups were identified: High Opposition (HO; 14.5%), Moderate Opposition (MO; 37.5%), and Low Opposition (LO; 48.0%). Children on the HO trajectory were found to be different from those on the MO and LO trajectories for numerous kindergarten risk factors: a) they tended to be boys with high family adversity; b) their mothers reported low warmth and high control; c) teachers rated them high on physical aggression, opposition, hyperactivity and low anxiety. Children who are likely to have chronic ODB throughout the elementary school years can be identified in kindergarten. Preventive interventions at school entry could probably help these children.

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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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