The hidden disaster: Domestic violence in the aftermath of natural disaster
Bibliographic record
Abstract
In countries similar to Australia, relationship violence increases in the wake of disasters. New Zealand police reported a 53 per cent rise in domestic violence after the Canterbury earthquake. In the US, studies documented a four-fold increase following two disasters and an astounding 98 per cent increase in physical victimisation of women after Hurricane Katrina, with authors concluding there was compelling evidence that intimate partner violence increased following large-scale disasters (Schumacher, et al., 2010). Yet there is a research gap on why this happens, and how increased violence may relate to disaster experiences. Women's Health Goulburn North East undertook the first Australian research into this phenomenon, previously overlooked in emergency planning and disaster reconstruction. Interviews with 30 women and 47 workers in Victoria after the 2009 Black Saturday bushfires provided evidence of increased domestic violence, even in the absence of sound quantitative data and in a context that silenced women. Community members, police, case managers, trauma psychologists and family violence workers empathised with traumatised and suffering men - men who may have been heroes in the fires - and encouraged women to wait it out. These responses compromise the principle that women and children always have the right to live free from violence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".