Adverse Childhood Experiences and Criminal Propensity Among Intimate Partner Violence Offenders
Bibliographic record
Abstract
Adverse childhood experiences (ACEs), defined as exposure to abuse and adverse household events, are prevalent among certain offenders including those who commit intimate partner violence (IPV). However, it is not clear how ACEs relate to criminal propensity among IPV offenders, who have been shown to exhibit less antisociality and institutional violence than other offenders. We compared 99 male offenders with a current or previous offense of IPV with 233 non-IPV violent offenders and 103 nonviolent offenders undergoing institutional forensic assessment. This convenience sample allowed for use of extensive psychosocial records as well as study of institutional violence. IPV offenders had the highest mean ACE score and more extensive criminal propensity on some measures (violent and nonviolent criminal history and psychopathy) than both other groups. ACEs were associated with most measures of criminal propensity in the whole sample but with only one (actuarial risk of violent recidivism) in the subsample of IPV offenders. Finding that ACEs are prevalent among IPV offenders even in this sample with extensive mental illness demonstrates the robustness of this phenomenon. IPV offenders, though, are similar to other violent offenders in this respect, and there is insufficient evidence that ACEs represent a criminogenic need among IPV offenders specifically. Further research could draw from the batterer typology literature and attend to IPV offenders' broader criminal careers.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".