Aligning policy and practice to implement CARE with FAIR through Indigenous Peoples’ protocols
Bibliographic record
Abstract
In 2019, members of the Global Indigenous Data Alliance (GIDA) published the CARE Principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) for Indigenous Data Governance (IDGov). CARE has since been referenced, leveraged, and adopted in various ways across disciplines and sectors worldwide. In this article, GIDA members from Aotearoa New Zealand, Australia, Canada, and the United States share examples of IDGov models that predate and emerged after the development of CARE. Together, we reflect upon the affordances and limitations of the broad uptake of CARE. We argue that renewed attention is needed to the original intent of CARE: to direct data actors to local communities’ protocols and frameworks for IDGov, and to transform institutional policies and practices to fortify Indigenous Peoples’ authority to control their data.
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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 | 0.419 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".