Promoting First Nations Sovereignty in Community-Engaged Research: Yarning and Relationship Building with Children’s Ground in Central Australia
Bibliographic record
Abstract
This Diversifying and Decolonizing Research article explores how you can achieve First Nations sovereignty and human rights through research partnership building and yarning interviews. Yarning is a special Australian First Nations term for qualitative interviews that are conducted in relational, conversational, and culturally safe ways. When done well, yarning can lead to elevated research data and relationships. Yarning can also produce personal and professional transformation for all people involved, including researchers. Achieving First Nations sovereignty in research requires coupling decolonising methods (yarning) with local First Nations leadership at all levels of research, from approval of research through to design, data collection, analysis, reporting, and translation. This case study describes how university researchers worked in partnership with Arrernte First Nations traditional custodians and researchers at Children’s Ground Central Australia (Arrernte language translation-Ampe-kenhe Ahelhe) to colead a community case study within the international research project titled The Remedy Project: First Nations Music as a Determinant of Health. We share stories and experiences about researcher attributes, values, principles (First Nations and non-First Nations). We share enabling processes within the research and offer practical examples to guide your future research practice.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".