Indigenous Peoples, Data Sovereignty and Self-Determination: Current Realities and Imperatives
Bibliographic record
Abstract
This study explores the current state and dynamics of the global Indigenous data sovereignty movement-the movement pressing for Indigenous peoples to have full control over the collection and governance of data relating to their lived realities. The article outlines the movement's place within the broader push for Indigenous self-determination; examines its links to big data, open data, intellectual property rights, and access and benefit-sharing; details a pioneering assertion of data sovereignty by Canada's First Nations; outlines relevant UN and international civil society processes; and examines the nascent movement in Africa. The study identifies a fundamental tension between the objectives of Indigenous data sovereignty and those of the open data movement, which does not directly cater for Indigenous peoples' full control over their data. The study also identifies the need for African Indigenous peoples to become more fully integrated into the global Indigenous data sovereignty movement.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".