One- Through Six-Component Solutions from Ratings on Familiar English Personality-Descriptive Adjectives
Bibliographic record
Abstract
We report solutions for one through six components for self-ratings (N = 559) on 449 familiar English personality-descriptive adjectives (see Lee & Ashton, 2008 ). The first unrotated component mainly contrasted desirable with undesirable characteristics. The varimax-rotated two-component solution contained dimensions closely resembling the Social Self-Regulation and Dynamism constructs of Saucier et al. (2014) . The three-component solution contained dimensions closely resembling the Affiliation, Dynamism, and Order constructs of De Raad et al. (2014) . In the four-component solution, an Emotional Stability dimension emerged, absorbing some variance from dimensions of the three-component solution. The five-component solution added an Intellect/Imagination/Unconventionality (Openness) component, and thus resembled the classic Big Five structure (e.g., Goldberg, 1990 ). In the six-component solution, the variance of the Big Five Agreeableness and Emotional Stability components was reorganized, producing components corresponding to HEXACO Agreeableness and to rotated variants of HEXACO Emotionality and Honesty-Humility. Solutions based on peer ratings (N = 303) were generally similar to those based on self-ratings, but showed a much larger first unrotated component.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".