Data Sovereignty and Development: How do Native Americans View Data Sharing by Tribal Governments?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Indigenous data sovereignty movement has arisen out of the ambition of Indigenous peoples to benefit from data-informed policy while preventing extractive and harmful research practices by external governments or researchers. Tribes exercise the sovereign authority to choose whether and when to share data with researchers and institutions outside their communities. To provide insight into how Indigenous peoples feel about data sharing, we document meaningful variation in a unique, nationwide survey of Native Americans. We find that respondents support their tribes in sharing data for economic benefit and that those who vote in tribal elections are particularly supportive. As tribal leaders, Native communities, and external research partners address potentially harmful data gaps and build Indigenous data resources, our findings suggest the importance of carefully considering and communicating the purpose of data collection and sharing. Broad benefit to Indigenous peoples’ economic well-being is one factor that likely increases support for data sharing.
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.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.014 |
| 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 it