Wangkiny Yirra “Speaking Up” project: First Nations women and children with disability and their experiences of family and domestic violence
Bibliographic record
Abstract
First Nations women and children with disability are at greater risk of family and domestic violence (FDV) and its consequences than their non-Indigenous peers. A recent report (Ringland et al., 2022) found that First Nations women with disability had the highest rates of victimisation of any group, with 34.4% recorded as being victims of crime. Despite this, the voices of First Nations people are largely missing from disability research in Australia (Dew et al., 2019). The purpose of this research was to engage with First Nations women and children and key stakeholders in Western Australia to: gain an understanding of their experiences of FDV, identify factors they believe open them up to the risk of harm, document their observations and experiences of barriers and/or enablers to seeking assistance and support, obtain their views on what works in currently available programs, and make recommendations for future culturally safe prevention and protection programs. Key findings: Research focus on experiences of FDV of First Nations women and children with disability appears to be growing, but is still limited within the broader body of research focused on First Nations women and children and FDV. First Nations people, wherever located, are significantly more likely than non-Indigenous people to be confronted with a range of barriers to service access, diagnosis and service delivery. Current strategies for prevention and support for First Nations women and children involved with the justice and child protection systems are demonstrably inadequate and harmful and must be reformed.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".