Psychopathy Checklist Score Predicts Negative Events during the Sentences of Prisoners with Hare Psychopathy: A Prospective Study at a German Prison
Bibliographic record
Abstract
OBJECTIVE: This study examined the predictive validity of the German translation of the Psychopathy Checklist-Screening Version (PCL-SV) for negative events during the course of the prison sentence of German prisoners. METHOD: Using the PCL-SV, we investigated 145 offenders in a German prison at the start of their sentences. We then compared the extreme groups identified by the PCL-SV--the high and low scorers--using a prospective design with respect to negative events and factors during the course of the sentences. This involved the standardized collection of data on both objective records of disciplinary incidents and subjective impressions from prison staff on the basis of operationalized criteria. RESULTS: The high scorers were involved in significantly more disciplinary incidents and were also rated significantly less favourably by prison staff than the low scorers. CONCLUSION: Until now, the PCL has only been shown to predict recidivism following release from prison. The results of our study show that the PCL also has predictive validity for problems during the course of the sentence. It is therefore recommended that the PCL be used routinely at the start of the prison sentence to estimate the likelihood of subsequent difficulties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".