The offending histories of homicide offenders: Are men who kill intimate partners distinct from men who kill other men?
Bibliographic record
Abstract
Objective: Limited research has studied the offending histories of homicide offenders across victim–offender relationships. An emphasis on offending histories may assist in identifying opportunities for criminal justice interventions, but it remains unclear whether these histories differ across different victim–offender relationship types. The aim of this study is to compare the offending histories of male intimate partner homicide (IPH) offenders and male-on-male homicide (MMH) offenders. Method: The data consist of self-reported offending histories collected through interviews with 203 men convicted of murder or manslaughter in Australia. IPH offenders (n = 68) were compared with MMH offenders (n = 135) across four areas (prevalence, frequency, versatility, and age of onset) using binary logistic regressions. Results: IPH offenders reported lower offending prevalence, less frequent and versatile offending, and later offending onset compared with MMH offenders. Conclusions: Both IPH and MMH offenders have a history of offending, though the extensiveness of this offending differs. Thus, IPH men may be less likely to come to the attention of the criminal justice system and, when they do, they may not be classified as “high risk.” The challenge is ensuring that other areas of risk are recognized and responded to in appropriate ways through effective screening or surveillance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".