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Record W2017301461 · doi:10.1002/ab.20309

Rough‐and‐tumble play and the regulation of aggression: an observational study of father–child play dyads

2009· article· en· W2017301461 on OpenAlexafffund
Joseph L. Flanders, Vanessa Leo, Daniel Paquette, Robert O. Pihl, Jean R. Séguin

Bibliographic record

VenueAggressive Behavior · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsAggressionObservational studyPsychologyHuman factors and ergonomicsInjury preventionPoison controlSuicide preventionDevelopmental psychologyOccupational safety and healthObservational methods in psychologyMedical emergencyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Rough-and-tumble play (RTP) is a common form of play between fathers and children. It has been suggested that RTP can contribute to the development of selfregulation. This study addressed the hypothesis that the frequency of father-child RTP is related to the frequency of physically aggressive behavior in early childhood. This relationship was expected to be moderated by the dominance relationship between father and son during play. Eighty-five children between the ages of 2 and 6 years were videotaped during a free-play session with their fathers in their homes and questionnaire data was collected about father-child RTP frequency during the past year. The play dyads were rated for the degree to which the father dominated play interactions. A significant statistical interaction revealed that RTP frequency was associated with higher levels of physical aggression in children whose fathers were less dominant. These results indicate that RTP is indeed related to physical aggression, though this relationship is moderated by the degree to which the father is a dominant playmate.

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)0.005
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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.066
GPT teacher head0.348
Teacher spread0.282 · 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

Citations133
Published2009
Admission routes2
Has abstractyes

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