A Case Law Survey of the Personality Assessment Inventory: Examining Its Role in Civil and Criminal Trials
Bibliographic record
Abstract
Although professional surveys suggest that the Personality Assessment Inventory (PAI; Morey, 1991) is a popular instrument among forensic and correctional psychologists, relatively little is known about the specific types of legal cases in which it is applied, the particular types of questions it is used to address, or the extent to which its admissibility has been at issue in court cases. Using a comprehensive legal database, we surveyed all published U.S., Canadian, European, and Australian criminal and civil cases in which the PAI was administered. The PAI appears to be introduced by examiners in a wide variety of civil (e.g., child custody, personal injury) and criminal (e.g., insanity, competence) cases to aid in the assessment of a broad range of psychopathology. Additionally, the PAI seems to be used frequently to assess questions concerning potential dissimulation and response styles. Surprisingly, the admissibility of the PAI into evidence was never at issue in any of the cases reviewed.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".