Forensic and Clinical Issues in the Assessment of Psychopathy
Bibliographic record
Abstract
Abstract The chapter begins with a discussion of the current clinical conceptualizations of psychopathy and basic diagnostic issues. The authors argue that the procedures used to assess psychopathy should reflect the decision‐making purpose of the assessment and the nature of the disorder being assessed. The second section reviews the most commonly used methods for assessing psychopathy. Clinical or expert rating scales of psychopathy (e.g., The Hare Psychopathy Checklist—Revised) are identified as having a better fit to the assessment task than are other assessment methods (e.g., self‐report questionnaires, structured diagnostic interviews). The third section identifies important professional and clinical issues that practitioners should keep in mind when assessing psychopathy. Issues related to the assessment of psychopathy among children and adolescents, and the assessment of risk for violence, are discussed. The fourth section examines a number of professional and clinical issues that arise as part of the clinical‐forensic assessment of psychopathy. These issues include failing to appropriately use accepted procedures for assessing psychopathy, using assessment procedures without adequate training and experience, and not establishing causal connections between diagnoses of psychopathy and the relevant legal issues. The authors provide practical recommendations for dealing with these and other clinical and professional issues. The chapter concludes with a discussion of issues that are priorities for future research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".