Criminal Careers Among Female Perpetrators of Family and Nonfamily Homicide in Australia
Bibliographic record
Abstract
Knowledge of women's pathways to serious offending, including homicide, is limited. This study contributes to a small but growing body of literature examining the criminal careers of serious female offenders by using interview data with females convicted of murder or manslaughter in Australia to examine various dimensions of their criminal careers, specifically, prevalence, frequency, age of onset, duration, and offending variety. In particular, in this study we compared criminal career dimensions across women who had killed a family member (e.g., intimate partner, children) and those whose victims were not part of the family unit (i.e., acquaintances or strangers). Our findings reveal differences between female homicide offenders who kill within and outside of the family unit. Although both groups had comparable overall lifetime prevalence of self-reported participation in criminal offending, findings indicate that participation among the family group was typically at low levels of frequency, of limited duration, and with relatively little variety in categories of offending. The family group also reported lower contact with the criminal justice system compared with the nonfamily group, and were less likely to have experienced some form of criminal/legal sanction in the 12 months prior to the homicide incident. This suggests that women who kill family members are more "conventional" than their nonfamily counterparts, in terms of having low and time-limited (i.e., short duration) lifetime participation in criminal offending.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".