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Record W4206162902 · doi:10.1111/spc3.12656

Practical recommendations for considering culture, race, and ethnicity in personality psychology

2022· article· en· W4206162902 on OpenAlex
Memoona Arshad, Joanne M. Chung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial and Personality Psychology Compass · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsEthnic groupEmic and eticPsychologyPersonalityRace (biology)Social psychologyFeelingSociologyGender studiesAnthropology

Abstract

fetched live from OpenAlex

Abstract Personality science is the study of the individual. It aims to understand what makes people similar to others, different from some, and unique to themselves. However, there is room for research in personality to more thoughtfully consider culture, race, and ethnicity in order to better understand individual differences in people’s patterns of thinking, feeling, and behaving. High impact personality journals rarely include such factors into the interpretation of results, and cross‐cultural and ethnic minority publications are limited within the discipline. This article offers a brief, non‐exhaustive overview of how culture, race, and ethnicity are examined in relation to personality, showing that: (1) social structures continue to be neglected in the research, (2) we can learn from research being conducted in neighboring areas, and (3) valuable work is already being done within personality psychology. We offer recommendations that emphasize community based participatory research methods, combined etic‐emic approaches, and contextualizing research findings to improve the consideration of culture, race, and ethnicity in personality research.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.365
GPT teacher head0.519
Teacher spread0.154 · 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