The assessment of psychopathic personality across settings
Bibliographic record
Abstract
The Psychopathy Checklist-Revised (PCL-R; Hare in The Hare Psychopathy Checklist—Revised. Mutli-Health Systems, Toronto, ON, 2003) has for many years been the gold standard psychopathy assessment, shaping the understanding of psychopathic personality. While the PCL-R remains a leading measure of psychopathy, some concerns have been raised that the instrument has become the sole representation of psychopathy. Recently, a number of measures and conceptual theories have emerged to both expand upon and counterbalance the large body of literature related to the PCL-R, this has included, self-report tools, clinical instruments, and research protocols. The PPI-R (Lilienfeld and Widows in Psychopathic Personality Inventory-Revised (PPI-R) Professional Manual. Psychological Assessment Resources, Florida, 2005) is one of the modern assessment tools of psychopathy, focused on personality, rather than encompassing criminal behaviour in the assessment of psychopathy. Other emerging instruments with promising application in criminal and noncriminal settings include, the Comprehensive Assessment of Psychopathic Personality (CAPP; Cooke et al., in International Journal of Forensic Mental Health, 11, 242–252, 2012) and Elemental Psychopathy Assessment (Lynam et al., in Psychological Assessment, 2010), while in research, the Triarchic Psychopathy Measure (TRiPM; Patrick, 2009) is a developing assessment tool with potential for clinical use. In the corporate setting, the Business-Scan (B-Scan; Mathieu et al., 2013) and the Corporate Personality Inventory-Revised (Fritzon et al., 2016) have been specifically developed, with the B-Scan 360 solely measuring psychopathy, and CPI-R examining problematic personality traits, including psychopathic characteristics. The chapter will review the body of assessment instruments examining psychopathic personality, explore strengths and weakness, and discuss the measures most suitable for use in the workplace.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".