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Record W2040205975 · doi:10.1037/a0020931

A social relations model of observed family negativity and positivity using a genetically informative sample.

2011· article· en· W2040205975 on OpenAlexaff
Jon Rasbash, Jennifer M. Jenkins, Thomas G. O’Connor, Jennifer L. Tackett, David Reiss

Bibliographic record

VenueJournal of Personality and Social Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of Toronto
FundersNational Institute of Mental HealthEconomic and Social Research CouncilWilliam T. Grant Foundation
KeywordsPsychologyNegativity effectDevelopmental psychologyMultilevel modelPartner effectsSiblingSocial psychologyNegativity biasVariance (accounting)Statistics

Abstract

fetched live from OpenAlex

The goal of this study was to investigate individual and relationship influences on expressions of negativity and positivity in families. Parents and adolescents were observed in a round-robin design in a sample of 687 families. Data were analyzed using a multilevel social relations model. In addition, genetic contributions were estimated for actor effects. Children showed higher mean levels of negativity and lower mean levels of positivity as actors than did parents. Mothers were found to express and elicit higher mean levels of positivity and negativity than fathers. Actor effects were much stronger than partner effects, accounting for between 18%-39% of the variance depending on the actor and the outcome. Genetic (35%) and shared environmental (19%) influences explained a substantial proportion of the actor effect variance for negativity. Dyadic reciprocities were lowest in dyads with a high power differential (i.e., parent-child dyads) and highest for dyads with equal power (sibling and marital dyads).

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)0.005
metaresearch head score (Gemma)0.014
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.325
GPT teacher head0.396
Teacher spread0.071 · 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

Citations58
Published2011
Admission routes1
Has abstractyes

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