The HEXACO Personality Space Before and After Re-Rotation to Approximate the Big Five Dimensions
Bibliographic record
Abstract
We re-oriented the HEXACO personality dimensions to approximate the Big Five, using two measures of the Big Five as targets in a derivation sample and then in cross-validation samples. The HEXACO approximations of Big Five Agreeableness represented blends of HEXACO Agreeableness, Emotionality, and Honesty-Humility. The HEXACO approximations of Big Five Neuroticism represented blends of Emotionality with low Agreeableness and low Extraversion. The residual sixth dimension, unrelated to the Big Five, contrasted Honesty-Humility with HEXACO Agreeableness. We then examined, in additional samples, some correlates of the original and re-rotated HEXACO dimensions. In the original HEXACO factor space, Honesty-Humility was the strongest correlate of unethical behaviors (selfishness and cheating), participant age, and "assumed similarity" to a friend or partner. Upon re-rotation of the HEXACO factors, associations involving these variables were divided between Big Five Agreeableness and the residual sixth dimension. Sex differences were mainly associated with Emotionality but after re-rotation of the HEXACO factors were divided between Big Five Agreeableness and Neuroticism. We discuss the relative merits of the original and Big Five-targeted HEXACO dimensions with reference to the practical utility of Big Five Agreeableness and Neuroticism and the simplicity and theoretical interpretability of the original HEXACO factors.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".