Using the HEXACO to explain the structure of borderline and psychopathic personality traits
Bibliographic record
Abstract
Research has examined the use of basic personality traits in describing problematic personality traits, such as borderline and psychopathic traits. Specifically, the Honesty-Humility factor of the HEXACO model of personality appears to account for a large proportion of the variance in these traits. The purpose of the present study was to examine whether the HEXACO model would similarly predict borderline traits. As found in previous research, psychopathic traits were predicted by low Honesty-Humility, Emotionality, Agreeableness, and Conscientiousness, whereas borderline traits were found to be negatively related to eXtraversion and Conscientiousness but had a significantly positive relationship with Emotionality. As Emotionality appeared to be a differential predictor in this study, future research should further examine how Emotionality distinguishes between the various problematic personality traits, which may aid potential treatments/therapies.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".