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Record W2588187610 · doi:10.1111/cdev.12754

An Examination of the Sibling Training Hypothesis for Disruptive Behavior in Early Childhood

2017· article· en· W2588187610 on OpenAlexafffund
Ella Daniel, André Plamondon, Jennifer M. Jenkins

Bibliographic record

VenueChild Development · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of TorontoUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsSiblingPsychologyDevelopmental psychologyAssociation (psychology)Sibling relationshipSimilarity (geometry)Operationalization

Abstract

fetched live from OpenAlex

Abstract Sibling training for disruptive behavior (one sibling teaching another disruptive behavior) is examined during early childhood. We used a conservative, recently developed, statistical model to identify sibling training. Sibling training was operationalized as the cross-lagged association between earlier child behavior and later sibling behavior, and differentiated from other reasons that contribute to sibling similarity. A three-wave longitudinal study tracked 916 children (Mage = 3.46, SD = 2.23) in 397 families using multi-informant data. Evidence for sibling training was found. Earlier younger siblings’ disruptive behavior predicted later lower levels of older siblings’ disruptive behavior. Thus, the sibling training found in early childhood was producing greater dissimilarity, rather than similarity, on disruptive behavior.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.311
Teacher spread0.253 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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