Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it