The Linguistic Output of Psychopathic Offenders During a PCL-R Interview
Bibliographic record
Abstract
We used text analysis software to examine the linguistic features of the speech of 21 psychopathic and 45 other offenders during the interview part of a Psychopathy Checklist–Revised (PCL-R) assessment. Regression analysis was run on the linguistic categories to determine which were the best predictors of psychopathy scores. Relative to the other offenders, psychopaths used more disfluencies (“you know”) and personal pronouns, made fewer references to other people (e.g., personal names, family), and were also less emotionally expressive. In particular, a low frequency of anxiety-related words and a more frequent use of personal pronouns were the most significant predictors of PCL-R scores and accounted for 25% of the variance. These findings for the first time afford a unique glimpse into the language produced during the PCL-R assessment interview. In addition to enhancing our understanding of psychopathic speech, these results may provide interviewers additional insights relevant to the assessment and therapeutic process.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".