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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".