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Record W1981976803 · doi:10.1017/thg.2014.80

Phenotypic, Genetic, and Environmental Relationships Between Self-Reported Talents and Measured Intelligence

2015· article· en· W1981976803 on OpenAlexaff
Julie Aitken Schermer, Andrew M. Johnson, Kerry L. Jang, Philip A. Vernon

Bibliographic record

VenueTwin Research and Human Genetics · 2015
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsPsychologyCreativityEmotional intelligenceDevelopmental psychologyTest (biology)Interpersonal communicationIntelligence quotientVerbal reasoningSocial psychologyCognitionBiologyEcology

Abstract

fetched live from OpenAlex

The relationship between self-report abilities and measured intelligence was examined at both the phenotypic (zero-order) level as well as at the genetic and environmental levels. Twins and siblings (N = 516) completed a timed intelligence test and a self-report ability questionnaire, which has previously been found to produce 10 factors, including: politics, interpersonal relationships, practical tasks, intellectual pursuits, academic skills, entrepreneur/business, domestic skills, vocal abilities, and creativity. At the phenotypic level, the correlations between the ability factor scores and intelligence ranged from 0.01 to 0.42 (between self-report academic abilities and verbal intelligence). Further analyses found that some of the phenotypic relationships between self-report ability scores and measured intelligence also had significant correlations at the genetic and environmental levels, suggesting that some of the observed relationships may be due to common genetic and/or environmental factors.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.343
GPT teacher head0.410
Teacher spread0.066 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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