Development of a Decolonising Framework for Aboriginal and Torres Strait Islander Health Policy Analysis in Australia
Bibliographic record
Abstract
Analysis of policies relevant to Aboriginal and Torres Strait Islander Peoples could help improve health outcomes—a critical challenge in Australia. While there are many health policy analysis frameworks, we did not find one which supported decolonising approaches across stages of the policy cycle. Generic frameworks were not based on decolonising approaches, and so risk perpetuating structural inequalities underpinning health disparities. Aboriginal and Torres Strait Islander specific frameworks articulated ways of working rather than addressing policy stages. We devised a new policy analysis framework by drawing upon Aboriginal and Torres Strait Islander specific and other policy analysis frameworks. The new framework can help critically analyse existing Aboriginal and Torres Strait Islander health policy and guide future policy making.
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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 | 0.129 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".