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Record W2049735318 · doi:10.1177/1073191113509686

Delineating Personality Traits in Childhood and Adolescence

2013· article· en· W2049735318 on OpenAlexafffund
Jennifer L. Tackett, Shauna C. Kushner, Filip De Fruyt, Ivan Mervielde

Bibliographic record

VenueAssessment · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPersonalityTemperamentPsychopathologyBig Five personality traitsDevelopmental psychologyPersonality Assessment InventoryConstruct (python library)Variance (accounting)Clinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The current investigation addressed several questions in the burgeoning area of child personality assessment. Specifically, the present study examined overlapping and nonoverlapping variance in two prominent measures of child personality assessment, followed by tests of convergent and divergent validity with child temperament and psychopathology. Informant report (72.1% mother) was obtained for a community sample of 803 youth (M age = 11.34 years; 51.6% female). The results revealed strong convergence between two empirically based measures of child personality traits, although some discrepancies were noted. The results from analyses predicting temperament and psychopathology were complex, suggesting that higher order child personality traits account for both shared and unique variance in these constructs, relative to one another. Overall, the current investigation provides a multifaceted contribution to evidence for construct validity of child personality traits and highlights the need for subsequent research in this area.

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)0.003
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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

Citations61
Published2013
Admission routes2
Has abstractyes

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