Unique and shared features of narcissistic and antisocial personality disorders: Implications for assessing and modeling externalizing traits
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine which, if any, features distinguish antisocial and narcissistic personality disorders (ASPD and NPD), two overlapping externalizing disorders. METHODS: A large sample of outpatients (N = 2,149) completed interview measures assessing personality pathology, other psychopathology, and impairment. The structure of antisocial and narcissistic traits was examined using both exploratory bifactor and traditional exploratory factor analytic approaches, and we examined relations for our emergent factors. RESULTS: Factor analytic results indicated that most narcissistic and antisocial traits were strongly overlapping, although some features emerged as relatively distinct (e.g., arrogance defining NPD). Factors modeling our specific bifactor dimensions showed very weak psychopathology and impairment relations. CONCLUSIONS: The structure of ASPD and NPD traits does not align neatly with Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Section II conceptualizations, Regardless of the factor analytic approach used. Our findings also indicate that specific dimensions defining these PDs show modest predictive power after accounting for a general externalizing dimension.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".