Recognising Indigenous data sovereignty and implementing Indigenous data governance at the Ngangk Yira Institute for Change
Bibliographic record
Abstract
The concepts of Indigenous data sovereignty and Indigenous data governance have attracted a great deal of attention over the last two decades. There have been several international symposia and roundtable discussions on the subject and a larger number of books and articles have been published. This paper focuses on developments in Australia, New Zealand, Canada and the United States of America, as countries with similar historical experiences, particularly in the relationship between the non-Indigenous colonisers and local Indigenous populations. Knowledge production related to Indigenous data sovereignty and data governance has direct relevance to the work of the Ngangk Yira Institute for Change (Ngangk Yira), Murdoch University – an Indigenous-led research intensive institute working in close collaboration with Aboriginal peoples and communities in Western Australia and nationally. This paper discusses development of theoretical positions on Indigenous data sovereignty and the mechanism by which this can be achieved, namely: Indigenous data governance. The variety of models, frameworks and principles are then examined. It also describes current projects being undertaken to assist Indigenous communities exercise sovereignty over their data and provides some examples of what can be achieved when research privileges an Indigenous world view and focuses on issues important to Indigenous people and communities. Finally, it explains how Ngangk Yira is incorporating the principles of Indigenous data sovereignty and governance into its research programs.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| 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".