Validating the B‐Scan Self: A self‐report measure of psychopathy in the workplace
Bibliographic record
Abstract
The surging interest in corporate psychopathy has underscored the need for a reliable and valid measure of psychopathic features that is suitable for research in organizational settings. The B‐Scan Self is a new self‐report measure of corporate psychopathy that was developed with Hare's Psychopathy Checklist‐Revised (PCL‐R) as a framework. Validity studies, using two independent Mechanical Turk samples, were designed to examine its factor structure and validity. Results indicated that B‐Scan Self facets were internally consistent and unidimentional and strongly related to another self‐report measure of psychopathy (SRP‐III). Confirmatory factor analyses supported a reliable fifteen facets and four‐factor model consistent with the PCL‐R four‐factor model of psychopathy. Furthermore, B‐Scan Self facets were positively correlated with the Dark Triad of personality traits and negatively correlated with FFM traits of Agreeableness and Conscientiousness. More importantly, B‐Scan Self facets presented the same pattern of correlations with FFM traits as the SRP‐III and different patterns than the two other Dark Triad measures. Although this constitutes the first validation study of the B‐Scan Self and more research is needed, we believe that these results are encouraging and that the B‐Scan Self provides an opportunity to study psychopathic features through the measure of work‐related behavior.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".