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Metatraits of the Big Five Differentially Predict Engagement and Restraint of Behavior

2009· article· en· W2005494435 on OpenAlexaff
Jacob B. Hirsh, Colin G. DeYoung, Jordan B. Peterson

Bibliographic record

VenueJournal of Personality · 2009
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Although initially believed to contain orthogonal dimensions, the Big Five personality taxonomy appears to have a replicable higher-order structure, with the metatrait of Plasticity reflecting the shared variance between Extraversion and Openness/Intellect, and the metatrait of Stability reflecting the shared variance among Neuroticism, Agreeableness, and Conscientiousness. These higher order traits have been theorized to relate to individual differences in the functioning of the dopamine and serotonin systems, respectively. As dopamine is associated with exploration and incentive-related action, and serotonin with satiety and constraint, this neuropharmacological trait theory has behavioral implications, which we tested in 307 adults by examining the association of a large number of behavioral acts with multi-informant reports of the metatraits. The frequencies of acts were consistently positively correlated with Plasticity and negatively correlated with Stability. At the broadest level of description, variation in human personality appears to reflect engagement and restraint of 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 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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.140
GPT teacher head0.426
Teacher spread0.286 · 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

Citations161
Published2009
Admission routes1
Has abstractyes

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