Understanding well‐being and safety for First Nations children and young people in the Riverland—Engaging with metic knowledge via a capability approach
Bibliographic record
Abstract
Abstract Aboriginal culture is both a strength and a protective factor for Aboriginal children; yet, we continue to see disparities in education, health and well‐being outcomes. To improve outcomes for Aboriginal children and families, local cultural ways of knowing, being and doing need to be incorporated into policy and practice. The strength‐based capability approach draws on the experiences, needs and values of people in context to understand the opportunities and freedoms to be and do what is culturally valued. Adopting a “capability approach,” First Nations peoples from the rural Riverland region of South Australia were involved in culturally safe yarning circles to explore aspirations for their children's well‐being and safety. In doing so, a better appreciation of the personal, social, structural and environmental factors that impinge on the achievement of well‐being and safety was possible. We highlight how a capability approach provides a valuable tool for engaging with and embracing metic knowledge in policymaking and practice. A more meaningful understanding of safety, well‐being and “the good life” in a community is critical for ensuring that policy and practice efforts can be directed in ways that create outcomes desired by Community.
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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 | 0.006 | 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 | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".