Storying Ways to Reflect on Power, Contestation, and Yarning Research Method Application
Bibliographic record
Abstract
Internationally within academia settler-colonial processes occur in various ways alongside a growth in the use of research methods conceived with Indigenous knowledges. However, most research environments and practices are built upon and privilege dominant non-Indigenous settler-colonial knowledge systems. It is within this power imbalance and contested space that Yarning research method is being applied and interpreted. Underpinned by an Indigenous Research Paradigm, we employed storying ways to examine researcher experiences of settler-colonialism and the Yarning research method. The story outlines challenges and pitfalls that researchers can fall into and critically examines how researchers can fail to recognise the depth of Indigenous knowledge embedded within the practice. This story is gifted by creating an imagined narrative interview with a character called Settler-Colonisation, whereby we identify a litany of settler-colonial processes impacting Yarning research. Scrutinising the epistemological and methodological practices and processes enacted in academia is imperative for better-informed application of Indigenous research methods and create sustainable research more generally.
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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 | 0.045 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".