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Record W2912437092 · doi:10.21827/ijpp.5.35039

Assessing personality across 13 countries using the California Adult Q-set

2019· article· en· W2912437092 on OpenAlexaffabout
Gwendolyn Gardiner, Esther Guillaume, Nick Stauner, Jaechang Bae, Gyu-Seong Han, Jung‐Soon Moon, Igor Bronin, Christina Ivanova, Joey T. Cheng, F. Köck, Sylvie Graf, Martina Hřebı́čková, Peter Haľama, Ryan Y. Hong, Paweł Izdebski, Clara Kulich, Fabio Lorenzi‐Cioldi, Lars Penke, Piotr Szarota, Jessica L. Tracy, Yu Yang, David C. Funder

Bibliographic record

VenueInternational Journal of Personality Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of British Columbia
FundersUniversity of California, RiversideNational Science Foundation
KeywordsNomothetic and idiographicNomotheticLikert scaleCzechPersonalityPsychologyBig Five personality traitsSet (abstract data type)ChinaSocial psychologyDemographyGeographyDevelopmental psychologySociologyComputer science

Abstract

fetched live from OpenAlex

The current project measures personality across cultures, for the first time using a forced-choice (or idiographic) assessment instrument - the California Adult Q-set (CAQ). Correlations among the average personality profiles across 13 countries (total N = 2,370) ranged from r = .69 to r = .98. The most similar averaged personality profiles were between USA/Canada; the least similar were South Korea/Russia/Poland and China/Russia. The Czech Republic had the most homogeneous personality descriptions and South Korea had the least. In further analyses, country differences in CAQ-derived Big Five scores were compared to results obtained from previous research using nomothetic Likert scales (i.e., the NEO; the BFI). The Big Five templates produced generally similar findings to previous research comparing the Big Five across countries using Likert-type methods.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.089
GPT teacher head0.471
Teacher spread0.382 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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