Bibliographic record
Abstract
Male violence against women and children is a pernicious global problem responsible for a high burden of injury, illness, and premature death across societies and cultures. Socio-cultural beliefs, attitudes, and practices underpin the conduct of perpetrators, targets, bystanders, and responding service providers, including police, health, and social welfare services. Bystanders’ willingness to act to help targets of family violence is a key dimension framing the social environment of using violence against family members. An anonymous internet survey of 464 Australians, mainly women, identified that around three-quarters of respondents would respond if they heard a cry for help from a nearby home. Most said they would call the police. The key deterrents to taking action were fears for their safety and their confidence that calling the police would lead to effective action. Despite their willingness to act, most believed that the typical Australian public would not do so. They attributed reluctance to take action to bystanders’ fears for their safety, beliefs that it was not their business, and not wanting to get involved. Respondents wanted more financial, housing, and legal support for victims of violence to end abusive relationships. Nationally consistent FDV laws, changes to media reporting, and school-based education were nominated as key strategies to prevent and reduce family and domestic violence.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".