Women as victims and offenders : incarcerated for murder in the Australian criminal justice system
Bibliographic record
Abstract
This study was undertaken in an effort to contribute to the limited knowledge of women who commit murder. Women account for approximately 10% of the total Australian homicides and according to Mouzos (2000), 20% of these female perpetrated homicides result in murder convictions. In her extensive study of female homicide offending in England, Brookman (2005) asserts that nearly two thirds of the victims of women who kill are intimates, to include violent partners and their own children. The other third of the victims consist largely of acquaintances and to lesser degree strangers (Brookman, 2005). This study strives to introduce further knowledge regarding women convicted of murder; the smaller subgroup of female homicide offenders of which less is known. It is comprised of women who killed intimates and non-intimates to include acquaintances. The study engages the narratives of seven women, all of whom were convicted of murder and serving lengthy sentences at the Dame Phyllis Frost Centre, a medium and maximum security prison that is located on the outskirts of Melbourne, Australia. The seven women fall largely outside of the characteristics of female homicide offenders as revealed in the studies from Australia’s National Homicide Monitoring Program (NHMP, 2007), from Canada by Hoffmann, Lavigne, and Dickie (1998) and research from the United States by Scott and Davies (2002). In this study there were no Indigenous women represented. Only one of the women had a previous criminal charge. The women were older on average than the prevailing demographics from western nations. Two of the women had substance abuse and co-occurring mental illness, which reflects a significant lower rate than the literature suggests. This study expands the current understanding of the phenomenon of women who murder. It communicates the narratives of seven women charged and convicted of murder as they attempt to understand their lives and identities. It moves the dialogue beyond the preponderance of feminist criminological research that examines motive and the relationship the woman has with her victim to the social discourses which dominate in her identity formation. This research found that in their attempt to create a favourable identity the women needed to engage with the master script of normative femininity through the feminisation of victimisation, motherhood and domesticity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".