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Sibling Personality Traits, Dyadic Gender Composition, and Their Association With Sibling Relationship Quality

2018· article· en· W2900951638 on OpenAlexaff
Noam Binnoon-Erez, Michelle Rodrigues, Michal Perlman, Jennifer Jenkins, Jennifer L. Tackett

Bibliographic record

VenueMerrill-Palmer Quarterly · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSiblingPsychologyPersonalitySimilarity (geometry)Association (psychology)Developmental psychologySibling relationshipBig Five personality traitsQuality (philosophy)Social psychology

Abstract

fetched live from OpenAlex

The current study contrasted two different hypotheses about the relationship between sibling personality and sibling relationship quality: absolute value and dyadic similarity. The absolute value hypothesis suggests that the level of one sibling’s personality will predict sibling relationship quality. The dyadic similarity hypothesis argues that the similarity between siblings on personality will be associated with sibling relationship quality. Observational data on child personality and maternal-report data on sibling relationship quality were collected on 321 sibling dyads (N = 642). Children were videotaped while completing five tasks, and personality traits were rated by independent raters based on thin-slice methodology. Support was found for the absolute value hypothesis but not the sibling similarity hypothesis: the personality traits of younger siblings predicted sibling relationship agonism, particularly when the older sibling was female. Findings suggest that older sisters are more sensitive to negativity in their younger siblings than are older brothers.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.351
Teacher spread0.272 · 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
Published2018
Admission routes1
Has abstractyes

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