Hungry like the wolf: A word‐pattern analysis of the language of psychopaths
Bibliographic record
Abstract
Purpose. This study used statistical text analysis to examine the features of crime narratives provided by psychopathic homicide offenders. Psychopathic speech was predicted to reflect an instrumental/predatory world view, unique socioemotional needs, and a poverty of affect. Methods. Two text analysis tools were used to examine the crime narratives of 14 psychopathic and 38 non‐psychopathic homicide offenders. Psychopathy was determined using the Psychopathy Checklist‐Revised (PCL‐R). The Wmatrix linguistic analysis tool () was used to examine parts of speech and semantic content while the Dictionary of Affect and Language (DAL) tool () was used to examine the emotional characteristics of the narratives. Results. Psychopaths (relative to their counterparts) included more rational cause‐and‐effect descriptors (e.g., ‘because’, ‘since’), focused on material needs (food, drink, money), and contained fewer references to social needs (family, religion/spirituality). Psychopaths’ speech contained a higher frequency of disfluencies (‘uh’, ‘um’) indicating that describing such a powerful, ‘emotional’ event to another person was relatively difficult for them. Finally, psychopaths used more past tense and less present tense verbs in their narrative, indicating a greater psychological detachment from the incident, and their language was less emotionally intense and pleasant. Conclusions. These language differences, presumably beyond conscious control, support the notion that psychopaths operate on a primitive but rational level.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".