Corporate psychopathy: Talking the walk
Bibliographic record
Abstract
There is a very large literature on the important role of psychopathy in the criminal justice system. We know much less about corporate psychopathy and its implications, in large part because of the difficulty in obtaining the active cooperation of business organizations. This has left us with only a few small-sample studies, anecdotes, and speculation. In this study, we had a unique opportunity to examine psychopathy and its correlates in a sample of 203 corporate professionals selected by their companies to participate in management development programs. The correlates included demographic and status variables, as well as in-house 360 degrees assessments and performance ratings. The prevalence of psychopathic traits-as measured by the Psychopathy Checklist-Revised (PCL-R) and a Psychopathy Checklist: Screening Version (PCL: SV) "equivalent"-was higher than that found in community samples. The results of confirmatory factor analysis (CFA) and structural equation modeling (SEM) indicated that the underlying latent structure of psychopathy in our corporate sample was consistent with that model found in community and offender studies. Psychopathy was positively associated with in-house ratings of charisma/presentation style (creativity, good strategic thinking and communication skills) but negatively associated with ratings of responsibility/performance (being a team player, management skills, and overall accomplishments).
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".