Good data relations key to Indigenous research sovereignty: A case study from Nunatsiavut
Bibliographic record
Abstract
Although researchers are committed to Indigenous data sovereignty in principle, they fall short in returning data and results to communities in which or with whom they conduct their research. This results in a misalignment in benefits of research toward researchers and settler institutions and away from Indigenous communities. To explore this, we conducted a case study analyzing the rate researchers returned data to Nunatsiavut, an autonomous area claimed by Inuit of Labrador, Canada. We assessed the data return rate for all research approved by the Nunatsiavut Government Research Advisory Committee between 2011 and 2021. In two-thirds of projects, researchers did not return the data they had collected. Based on our results and their contextualization with researchers and Nunatsiavut Research Centre staff members, we compiled recommendations for researchers, academia, government bodies, funding bodies, and Indigenous research governance boards. These recommendations aim to facilitate data return, thus putting data sovereignty into practice.
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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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".