Bibliographic record
Abstract
While personality trait assessments are widely used in candidate selection, coaching and occupational counseling, little published research has systematically compared occupations in personality traits. Using a comprehensive personality assessment, we mapped 263 occupations in the self-reported Big Five domains and various personality nuances in a sample of 68,540 individuals, cross-validating the findings in informant-ratings of 19,989 individuals. Controlling for age and gender, occupations accounted for 2% to 7% of the variance in Big Five domains and up to 12% in nuances. Most occupations’ average trait levels were intuitive, replicated in informant ratings, and were consistent with those previously obtained with a brief personality assessment in a different sociocultural context. They also meaningfully tracked O*NET’s work style ratings, and tended to cluster along the ISCO’s hierarchical framework, albeit with several (meaningful) exceptions. Finally, occupations with higher average levels of traits typically linked to better job performance tended to be more homogeneous in these traits, suggesting that jobs with higher-performing incumbents are often more selective for the traits. We provide an interactive application for exploring the results (https://apps.psych.ut.ee/JobProfiles/) and discuss the findings’ theoretical and practical implications. Test your personality traits in relation to these jobs: https://whichjob.me
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".