Practical recommendations for considering culture, race, and ethnicity in personality psychology
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.270 | 0.405 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.025 | 0.035 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".