Advancing Indigenous data governance through a shared understanding in Paulatuk, Inuvialuit Settlement Region
Bibliographic record
Abstract
In the Canadian Arctic, we posit that locally-relevant Indigenous data governance frameworks are necessary in light of a paucity of guiding practices and policies for environmental researchers working in partnership with communities. To centre data governance decision-making in a community and to support Indigenous self-determination as affirmed in federal commitments, Fisheries and Oceans Canada researchers and the Paulatuk Hunters and Trappers Committee (Paulatuk, Inuvialuit Settlement Region) co-developed a data governance Statement of Shared Understanding for Traditional Knowledge Documentation specific to an interview project. We detail the steps and dialogue that characterized the creation of this statement over several months, so that others may build from these efforts when appropriate. Second, we highlight five emergent considerations that may strengthen future data governance efforts and inform policy, including: community and project context, the changing digital landscape, individual and collective knowledge protections, planned project outputs, and confidentiality and anonymity nuances. We offer these insights to advance evolving Indigenous data governance conversations, initiatives, and policies in institutional and community spaces.
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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 | 0.055 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.032 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".