Data as a Strategic Resource: Self-determination, Governance, and the Data Challenge for Indigenous Nations in the United States
Bibliographic record
Abstract
Data about Indigenous populations in the United States are inconsistent and irrelevant. Federal and state governments and researchers direct most collection, analysis, and use of data about U.S. Indigenous populations. Indigenous Peoples’ justified mistrust further complicates the collection and use of these data. Nonetheless, tribal leaders and communities depend on these data to inform decision making. Reliance on data that do not reflect tribal needs, priorities, and self-conceptions threatens tribal self-determination. Tribal data sovereignty through governance of data on Indigenous populations is long overdue. This article provides two case studies of the Ysleta del Sur Pueblo and Cheyenne River Sioux Tribe and their demographic and socioeconomic data initiatives to create locally and culturally relevant data for decision making.
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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 | 0.143 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.010 |
| 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".