Impulsive versus premeditated aggression in the prediction of violent criminal recidivism
Bibliographic record
Abstract
Past aggression is a potent predictor of future aggression and informs the prediction of violent criminal recidivism. However, aggression is a heterogeneous construct and different types of aggression may confer different levels of risk for future violence. In this prospective study of 91 adults in a pretrial diversion program, we examined (a) premeditated versus impulsive aggression in the prediction of violent recidivism during a one-year follow-up period, and (b) whether either type of aggression would have incremental validity in the prediction of violent recidivism after taking into account frequency of past general aggression. Findings indicate that premeditated, but not impulsive, aggression predicts violent recidivism. Moreover, premeditated aggression remained a predictor of recidivism even with general aggression frequency in the model. Results provide preliminary evidence that the assessment of premeditated aggression provides relevant information for the management of violent offenders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".