Domestic and family violence and police negligence
Bibliographic record
Abstract
Domestic and family violence in Australia has received unprecedented attention over the past few years. A number of recent reports and reviews have identified that improved policing is key to enhancing the safety of women and children. In response to these reports, and in recognition that police are often the first to respond to domestic violence, a number of jurisdictions have strengthened police powers and in some cases mandated police responses. This article draws on a qualitative study of victim’s experiences of police responses to domestic violence in order to identify the extent and breadth of the problems that continue to plague police responses to domestic violence in Queensland, in spite of legislative change. The article then uses Queensland as a case study to consider whether a victim of domestic violence, who claims that the police failed to adequately respond to or deal with their request for assistance, would be able to successfully take a private civil action against the police in Australia, specifically in the tort of negligence. Recent cases decided in the United Kingdom and Canada, changing community attitudes and the enhanced police powers that have been introduced in Queensland and elsewhere to ensure police better respond to domestic and family violence reopen questions about the current position in Australia.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".