Indigenous data sovereignty in Australian higher education: paving the way for First Nations’ self-determination
Bibliographic record
Abstract
The Australian Universities Accord Final Report offers a historic yet insufficient opportunity to advance Indigenous self-determination in higher education. Its goals will remain hollow without dismantling the entrenched colonial foundations embedded in universities’ governance and data practices. This paper demands that Indigenous data sovereignty – the inherent right of Indigenous peoples to control data about their communities, knowledge systems, and territories – become the unyielding cornerstone of university transformation. Building on the critical work of Indigenous scholars and decolonial theorists, it presents a radical agenda: (1) advance Indigenous data governance despite systemic constraints, (2) overhaul exploitative research protocols, (3) embed Indigenous knowledge systems, (4) invest in Indigenous data infrastructures, and (5) forge alliances that centre Indigenous nationhood. This agenda challenges universities to abandon symbolic reforms and confront their colonial legacies. By embracing Indigenous data sovereignty, universities can honour their obligations and lead the charge towards a just, humane, and decolonised future.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.015 |
| 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".