Committing Homicide After Drinking: The Characteristics of Self-Reported Alcohol-Involved Homicide Offending
Bibliographic record
Abstract
It is well established that alcohol use is associated with homicide (e.g. Kuhns et al. 2014; Parker et al. 2011). However, much of our knowledge of alcohol-involved homicide is based on official data. Self-report data obtained directly from offenders about their consumption of alcohol immediately prior to committing homicide provide valuable information that complements data from official sources such as details emerging from police investigations and court proceedings. However, such data are rare. This study analyzes self-report data collected through face-to-face interviews with 205 men and women convicted of murder or manslaughter in Australia, of whom almost half (43.4%) reported use of alcohol prior to the homicide. The strongest predictor of alcohol-involved homicide was ongoing alcohol problems, speaking to the importance of prevention strategies targeting the entrenched nature of substance misuse. Furthermore, alcohol-involved homicides were nighttime events, committed in public places by older offenders using knives. Though such variables appear indicative of impulsiveness, a measure of self-control did not distinguish between alcohol-involved and not alcohol-involved homicide. Further investigations into the role of self-control on alcohol and violence are necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".