Successful Strategies to Improve Access to Justice for Women Who Kill Their Abusers
Bibliographic record
Abstract
This issue of the International Journal for Crime, Justice and Social Democracy has its origins in an inspiring and successful international workshop held at Deakin University Downtown in February 2023 (Successful Strategies for Improving Access to Justice for Women Who Kill Their Abusers). It is in this context that the international workshop drew together practitioners (practising lawyers, family violence experts, psych- experts) and researchers from a range of international jurisdictions (England, Scotland, New Zealand, Canada, United States, Germany and Australia) and disciplines (criminology, law, socio-legal studies, gender studies, Māori health and Indigenous studies and education), to share insights about their efforts to improve legal understandings of women’s experiences of intimate partner violence (IPV), their use of fatal force against an abusive partner and their self-defence claims. Contributors to this special issue highlight, for example, the power of positive collaboration between academics, activists, lawyers, journalists and the women themselves. They identify strategies to challenge prosecutors’ decisions to prosecute Aboriginal women in the absence of evidence capable of disproving self-defence, and to identify key evidentiary checkpoints to enhance women’s access to self-defence and improve their chances of acquittal. They argue for the need to build the workforce and capacity of experts with frontline experience in IPV, emphasise the importance of understanding IPV through the lens of social entrapment, and propose targeted training to skill up practitioners to more effectively utilise the family violence evidence provisions available in some jurisdictions throughout the whole court process. Finally, they explore potential avenues for further reform drawing on successes and failures in advocacy and litigation across international jurisdictions.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".