Identifying Useful Approaches to the Governance of Indigenous Data
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
Questions of data governance occur in all contexts. Arguably, they become especially pressing for data concerning Indigenous people. Long-standing colonial relationships, experiences of vulnerability to decision-makers, claims of jurisdiction, and concerns about collective privacy become significant in considering how and by whom data concerning Indigenous people should be governed. Also significant is the on going need on the part of governments to access and use such data to plan, monitor, and account for programs involving Indigenous people. This exploratory policy article seeks to inform efforts to improve the governance of data between governments and Indigenous organizations and communities – especially the federal government and First Nations in Canada. It describes a spectrum of models arising from the growing literature on data governance in the corporate and public sectors as well as overarching approaches articulated by Indigenous organizations. After outlining certain practical considerations in negotiating data sharing agreements, the article presents a selection of promising initiatives in indigenous data governance undertaken in Canada, the United States, and Australia.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 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 it