Unity and Diversity of NEO-PI Personality Profiles of 162 Phenotypes
Bibliographic record
Abstract
For over 30 years, detailed personality-phenotype association profiles based on the NEO Personality Inventory (NEO PI) have been published, yet the phenotype profiles have been rarely linked across studies as has been done in brain imaging and genetics. Here, we apply a similar method to explore the similarities, differences, and healthfulness of various phenotypes regarding their NEO PI correlates. We systematically searched for profiles of NEO PI-phenotype correlations across various phenotype domains. Our initial dataset comprised 311 profiles from 90 papers. After standardising the metrics and meta-analysing similar profiles, the data was consolidated into 162 distinct phenotypes. Cluster analysis resulted in five broad cross-domain clusters, characterised by mental health strains, psychopathology dimensions, educational attainment, and health-related behaviours. Most clusters showed moderate deviation from an expert-rated ‘healthy’ personality profile. Furthermore, we replicated ‘personality correlations’, exploring the similarities between uncontrolled eating and addictive phenotypes, accounting for sample sizes of the profiles as well as the correlations among facets. Our findings demonstrate that comparing personality-phenotype profiles across studies offers novel insights and possibilities for theory-testing and exploratory comparisons.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".