Indigenous Data Sovereignty: Applying It By, With, For, and Through Indigenous Evaluators and Evaluations
Bibliographic record
Abstract
Indigenous data sovereignty (IDS) is a relatively recent term and global movement that originated from the Global Indigenous Data Alliance (GIDA) in 2015 when the formal international network was created. The global North and South have representation in the GIDA through the nation-states including the Maiam nayri Wingara Collective (Australia), Te Mana Raraunga Maori Data Sovereignty Network (Aotearoa New Zealand), and the United States Indigenous Data Sovereignty Network. IDS is founded on time-immemorial knowledge, wisdom, and lifeways of Indigenous peoples and First Nations globally including Tribal treaties, Tribal constitutions, the United Nations Declaration of the Rights of Indigenous Peoples, and other human and natural rights laws. The authors share an overview of IDS; the experiences they had presenting and learning at the 2024 IDS Conference in Tucson, Arizona, USA; and examples and applications of IDS to their direct evaluation, editorial, and publication policy work. They conclude the article with their reflections on how the field of evaluation should be aware and inclusive as it moves forward into the future applying IDS to evaluative thinking, theory, policies, funding, and practice.
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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 | 0.474 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.039 | 0.035 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".