Bibliographic record
Abstract
Decolonising Research Practices: Creating a political space to learn about ethics and care from Māori 1 .Decolonising practices in health, education and research have been high on the indigenous agenda for many years.Assuming expertise from a Western perspective is assuming a dominant and colonising position, one which fails to recognise the expertise and world view of colonised societies, and subsequent implications.Johnstone (1) recently wrote about the need for decolonising nursing ethics, adding to a growing movement reclaiming indigenous knowledge in Aotearoa New Zealand, Canada, and more recently Australia.Having worked in Aotearoa New Zealand from 2006 -2015, I was fortunate to build partnerships with Māori mental health services users Te Kowhao in mental health education, and with Dr Amohia Boulton in research.I learned a lot about relationality, and the priorities that govern Māori life.Learning from Māori was key to understanding how to contribute in a
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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 | 0.025 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.034 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".