Personality characteristics below facets (meta-analysis)
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.
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 six countries (Australia, Canada, Czech Republic, Denmark, Japan, and United States). The same personality measureâ240-item Revised NEO Personality Inventoryâand 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., itemsâ 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 three item properties were inter-correlated, suggesting that items systematically differed in the degree of reflecting valid unique variance. Also, associations of itemsâ 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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.908 | 0.745 |
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 it