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Record W4385851059 · doi:10.1111/cdev.13994

Reconsidering the failure model: Using a genetically controlled design to assess the spread of problems from reactive aggression to internalizing symptoms through peer rejection across the primary school years

2023· article· en· W4385851059 on OpenAlexafffund
Sharon Faur, Olivia Valdes, Frank Vitaro, Mara Brendgen, Michel Boivin, Brett Laursen

Bibliographic record

VenueChild Development · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité LavalUniversité du Québec à MontréalUniversité de Montréal
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyAggressionDevelopmental psychologyTwin studyGenetic modelStructural equation modelingMonozygotic twinHuman factors and ergonomicsPoison controlClinical psychologyHeritabilityGeneticsMedicine

Abstract

fetched live from OpenAlex

According to the failure model (Patterson & Capaldi, 1990), peer rejection is the intermediary link between problem behaviors and internalizing symptoms. The present study tested the model with 464 monozygotic and same-sex dizygotic twin pairs (234 female, 230 male dyads). Teacher-reported reactive aggression and internalizing symptoms, and peer-reported peer rejection were collected at ages 6, 7, and 10 (from 2001 to 2008). Support for the failure model emerged in conventional non-genetically controlled analyses, but not twin-difference score analyses (which remove shared environmental and genetic contributions). Univariate biometric models attributed minimal variance in failure model variables to shared environmental factors, suggesting that genetic factors play an important unacknowledged role in developmental pathways historically ascribed to nonshared experiences in the failure model.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.118
GPT teacher head0.330
Teacher spread0.212 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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