Personality characteristics below facets: A replication and meta-analysis of cross-rater agreement, rank-order stability, heritability, and utility of personality nuances.
Bibliographic record
Abstract
Mõttus and colleagues (2017) reported evidence that the unique variance in specific personality characteristics captured by single descriptive items often displayed trait-like properties of cross-rater agreement, rank-order stability, and heritability. They suggested that the personality hierarchy should be extended below facets to incorporate these specific characteristics, called personality nuances. The present study attempted to replicate these findings, employing data from 6,287 individuals from 6 countries (Australia, Canada, Czech Republic, Denmark, Japan, and United States). The same personality measure-240-item Revised NEO Personality Inventory-and statistical procedures were used. The present findings closely replicated the original results. When the original and current results were meta-analyzed, the unique variance of nearly all items (i.e., items' scores residualized for all broader personality traits) showed statistically significant cross-rater agreement (median = .12) and rank-order stability over an average of 12 years (median = .24), and the unique variance of the majority of items had a significant heritable component (median = .14). These 3 item properties were intercorrelated, suggesting that items systematically differed in the degree of reflecting valid unique variance. Also, associations of items' unique variance with age, gender, and body mass index (BMI) replicated across samples and tracked with the original findings. Moreover, associations between item residuals and BMI obtained from one group of people allowed for a significant incremental prediction of BMI in an independent sample. Overall, these findings reinforce the hypotheses that nuances constitute the building blocks of the personality trait hierarchy, their properties are robust and they can be useful. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.046 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".