Factor structure of the B-Scan 360: A measure of corporate psychopathy.
Bibliographic record
Abstract
Psychopathy is a clinical construct defined by a cluster of personality traits and behaviors, including grandiosity, egocentricity, deceptiveness, shallow emotions, lack of empathy or remorse, irresponsibility, impulsivity, and a tendency to ignore or violate social norms. The majority of empirical research on psychopathy involves forensic populations most commonly assessed with the Psychopathy Checklist-Revised (PCL-R), a 20-item rating scale that measures 4 related factors or dimensions (Interpersonal, Affective, Lifestyle, and Antisocial) that underpin the superordinate construct of psychopathy. Recently, researchers have turned their attention to the nature and implications of psychopathic features in the workplace. This research has been hampered by the lack of an assessment tool geared to the corporate/organizational world. Here we describe the B-Scan 360, an instrument that uses ratings of others to measure psychopathic features in workplace settings. In this study, large samples of participants used an online survey system to rate their supervisors on the B-Scan 360. Exploratory and confirmatory factor analyses supported a reliable 20-item, 4-factor model that is consistent with the PCL-R 4-factor model of psychopathy. Although more research is needed before the B-Scan 360 can be used in organizational settings, we believe that these results represent an important step forward in the study of corporate psychopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".