A numbers game: lack of gendered data impedes prevention of disaster-related family violence
Bibliographic record
Abstract
Issue addressed The lack of a systematic approach to collecting family violence data after a disaster impedes family violence prevention and response efforts. Without evidence, there is little chance that interventions will be planned and implemented to address increased family violence after disasters. Methods A literature review of international and Australian gendered disaster research was conducted, with a focus on family violence following disasters in developed countries. A case study was prepared exploring the complexity of gathering data about family violence in the aftermath of the Victorian Black Saturday bushfires. Results Although increases in family violence in the aftermath of the Black Saturday bushfire were observed and anecdotally reported by funded family violence agencies, recovery authorities and community leaders, attempts by Women's Health in the North and the researchers to quantify the increase were unsuccessful. The fragmented nature of the family violence data that was collected was a consequence of inconsistent data recording practices and the complex and multifaceted nature of the recovery effort. Conclusions Health promotion theory and service planning demand a sound evidence base for interventions. In the absence of this, family violence following disasters will continue to be overlooked in the face of ‘urgent’ needs. So what? Disaster research that considers family violence should be conducted in Australia. Government and non-government agencies involved in disaster planning, recovery and reconstruction need to focus on producing data on the occurrence of family violence in this context, and train emergency and reconstruction workers in the dynamics of family 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 | 0.049 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".