Bibliographic record
Abstract
Based on a national analysis of Indigenous family violence, the 2001 monograph on ‘Violence in Indigenous Communities’ by the author and his colleagues for the Australian Attorney-General's Department called for government agencies to ‘take a regional approach to supporting and co-ordinating local community initiatives’ together with ‘partnerships between Indigenous program personnel and mainstream services...’ (Memmott et al., 2001, p. 4). This current article reports on regional aspects of two subsequent pieces of research by the author, one in the Barkly Region of central-east Northern Territory for Anyinginyi Health Aboriginal Corporation (2007) and the other in the Torres Strait for the Queensland Department of Communities (2008). The research findings from both of these studies develop the case for government policy to accommodate regional approaches to Indigenous family violence due to combinations of geographic and culturally specific causal factors. The importance of nurturing social and cultural capital in Indigenous communities to strengthen social values, leadership and cohesion in addressing Indigenous violence will be emphasised. Some comment will be made on the role of underlying factors (‘deep historical circumstances’) in contributing to violence, in conjunction with precipitating causes and situational factors, the former being somewhat downplayed in policy debate over the period of the Howard government.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".