Development, Validation, and Faking-Resistance of an Implicit Measure of Psychopathy in the Workplace
Bibliographic record
Abstract
Researchers have called for faking-resistant measures of psychopathic personality that can be self-administered in high-stakes contexts (e.g. hiring). We developed and validated an implicit measure of psychopathy contextualized in workplace situations. We first detail how the measure is framed, conceptualized, and rooted in psychopathy literature. We then describe the item development process, and Study 1 involves expert review and refining the item list. In Study 2 (N = 396), we examine internal consistency and factor structure for a 22-item version of the measure. In Study 3 (N = 251), we demonstrate test-retest reliability, construct-related validity, and provide initial evidence for criterion-related validity through a two-wave study. Study 4 analyzes the measure using item response theory, based on a sample of 6,746 job seekers, demonstrating effectiveness for measuring high levels of psychopathy. In Study 5 (N = 219) we provide evidence of faking-resistance and criterion-related validity with behavioral (two weeks later) and self-report (one year later) outcomes. Finally, Study 6 provides promising evidence of incremental validity using an organizational sample (N = 615). Overall, scores on this new implicit measure are reliable, with acceptable construct-related, criterion-related, and incremental validity, while also being faking-resistant. Implications for use in workplace settings are discussed.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".