Screening for Dark Personalities
Bibliographic record
Abstract
Abstract. Consensus is emerging that the constellation of dark personalities should include the sadistic personality. To build a four-factor measure, we modified and extended the Short Dark Triad (SD3) measure to include sadism. A series of three studies yielded the Short Dark Tetrad (SD4), a four subscale inventory with 7 items per construct. Study 1 ( N = 868) applied exploratory factor analysis (EFA) to a diverse 48-item pool using data collected on MTurk. A 4-factor solution revealed a separate sadism factor, as well as a shifted Dark Triad. Study 2 ( N = 999 students) applied EFA to a reduced 37-item set. Associations with adjustment and sex drive provided insight into unique personality dynamics of the four constructs. In Study 3 ( N = 660), a confirmatory factor analysis (CFA) of the final 28 items showed acceptable fit for a four-factor solution. Moreover, the resulting 7-item subscales each showed coherent links with the Big Five and adjustment. In sum, the four-factor structure replicated across student and community samples. Although they overlap to a moderate degree, the four subscales show distinctive correlates – even with a control for acquiescence. We also uncovered a novel link between sadism and sexuality, but no association with maladjustment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".