Country Is Yarning to Me: Worldview, Health and Well-Being Amongst Australian First Nations People
Bibliographic record
Abstract
Abstract Health inequalities experienced by Australian First Nations People are amongst the most marked in the world, with First Nations People dying some ten years earlier than non-Indigenous Australians. The failure of existing responses to health inequalities suggests new knowledges and questions that need to be explored. It is likely that these new knowledges sit outside of western research or practice paradigms. Through the Indigenous practice of yarning, the importance of worldview and Country emerged as an under-acknowledged social determinant of Australian First Nations People well-being. Yarning is a process of storytelling that involves both sound and silence. It requires embodied deep listening through which stories emerge that create new knowledge and understanding. We anchor our learning by re-telling John’s creation story, a story of healing through discovering his Aboriginal Worldview through reconnecting to Country. Country for First Nations People is more than a physical place; it is a place of belonging and a way of believing. We argue for the recognition of trauma, recognition of diversity and the use of yarning in social work practice. We conclude that reconnecting to Aboriginal Worldview provides hopeful insights into the well-being of Australia’s First Nations People and the social determinants of health.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".