<i>Debakarn Koorliny Wangkiny</i> : steady walking and talking using first nations-led participatory action research methodologies to build relationships
Bibliographic record
Abstract
Aboriginal participatory action research (APAR) has an ethical focus that corrects the imbalances of colonisation through participation and shared decision-making to position people, place, and intention at the centre of research. APAR supports researchers to respond to the community's local rhythms and culture. APAR supports researchers to respond to the community's local rhythms and culture. First Nations scholars and their allies do this in a way that decolonises mainstream approaches in research to disrupt its cherished ideals and endeavours. How these knowledges are co-created and translated is also critically scrutinised. We are a team of intercultural researchers working with community and mainstream health service providers to improve service access, responsiveness, and Aboriginal client outcomes. Our article begins with an overview of the APAR literature and pays homage to the decolonising scholarship that champions Aboriginal ways of knowing, being, and doing. We present a research program where Aboriginal Elders, as cultural guides, hold the research through storying and cultural experiences that have deepened relationships between services and the local Aboriginal community. We conclude with implications of a community-led engagement framework underpinned by a relational methodology that reflects the nuances of knowledge translation through a co-creation of new knowledge and knowledge exchange.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".