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Record W2018816279 · doi:10.1037/0022-3514.93.2.298

Prefrontal cognitive ability, intelligence, Big Five personality, and the prediction of advanced academic and workplace performance.

2007· article· en· W2018816279 on OpenAlexaff
Daniel M. Higgins, Jordan B. Peterson, Robert O. Pihl, Alice G. M. Lee

Bibliographic record

VenueJournal of Personality and Social Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsConscientiousnessPsychologyPersonalityJob performanceBig Five personality traitsCognitionSocial psychologyApplied psychologyJob satisfactionPsychiatryExtraversion and introversion

Abstract

fetched live from OpenAlex

Studies 1 and 2 assessed performance on a battery of dorsolateral prefrontal cognitive ability (D-PFCA) tests, personality, psychometric intelligence, and academic performance (AP) in 2 undergraduate samples. In Studies 1 and 2, AP was correlated with D-PFCA (r=.37, p<.01, and r=.33, p<.01, respectively), IQ (r=.24, p<.05, and r=.38, p<.01, respectively), and Conscientiousness (r=.26, p<.05, and r=.37, p<.01, respectively). D-PFCA remained significant in regression analyses controlling for intelligence (or g) and personality. Studies 3 and 4 assessed D-PFCA, personality, and workplace performance among (a) managerial-administrative workers and (b) factory floor workers at a manufacturing company. Prefrontal cognitive ability correlated with supervisor ratings of manager performance at values of r ranging from .42 to .57 (ps<.001), depending on experience, and with factory floor performance at pr=.21 (p=.02), after controlling for experience, age, and education. Conscientiousness correlated with factory floor performance at r=.23.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.387
Teacher spread0.320 · 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

Citations186
Published2007
Admission routes1
Has abstractyes

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