Legal Processes and Gendered Violence: Cross-Applications for Domestic Violence Protection Orders
Bibliographic record
Abstract
Official statistics consistently demonstrate the gendered nature of domestic violence ('DV'). A recent report states that violence against women affected one in three Australian women and cost the economy around $13.6 billion in 2009 with women being most harmed. Over the past two decades, the legal response to DV has been increasingly focused on civil domestic violence protection order legislation in Australia, Canada, the United Kingdom and the United States. Domestic violence protection orders ('DVPOs') are now the most common legal remedy sought by, or on behalf of, women experiencing DV. In all Australian states a civil DVPO can be made by the lower courts to restrict and prohibit a perpetrator of DV (a respondent) from committing further acts of violence against a person (an aggrieved). While in the vast majority of these cases, applications are lodged by or on behalf of one partner (typically a female) against the other partner (typically a male), in a smaller proportion of cases both partners seek protection orders against each other. In some cases these 'cross-applications' will result in 'cross-orders', or mutual protection orders being made by the court resulting in a DVPO against both parties. In the event of a cross-order, there are conditions attached to each partner's DVPO. In Queensland, all DVPOs will include a condition that the party be of good behaviour toward the aggrieved and individual DVPOs may also include other conditions, for example, a person may be prohibited from making contact with the aggrieved and from entering specified premises.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".