The Kahnawà:ke Schools’ Diabetes Prevention Project: Perspectives on Data Sovereignty in Indigenous Community-Academic Partnered Health Research
Bibliographic record
Abstract
Introduction. Data sovereignty in Indigenous research recognizes the authority of Indigenous peoples over research data and processes. Data sovereignty is an important tool for the self-determination of Indigenous communities, as it fosters the collection of relevant data that aligns with community goals and interests. While guidelines exist to support Indigenous community-academic research partnerships in facilitating community-controlled research and data sovereignty, these guidelines often lack practical clarity, and could benefit from practical examples of successful partnerships. Purpose. The purpose of this study is to explore how The Kahnawà:ke Schools’ Diabetes Prevention Project (KSDPP), a mature and successful Indigenous community-academic research partnership, operationalizes data sovereignty and facilitates research in a community-controlled manner. Methods. Eight individuals having various roles within the KSDPP participated in semi-structured interviews. The interview guide was theory-informed using indicators of data sovereignty extracted from literature on data sovereignty in community-controlled research. The KSDPP Code of Research Ethics and letters of information/consent forms from past KSDPP studies were also reviewed. These data were collected and analyzed using a thematic analysis. Results. Seven themes were identified related to research benefits, collaboration and communication, capacity and growth, respectful relationships, data stewardship, defining community control, and growth through adversity. Discussion. The community controls the KSDPP research process and accordingly the content, management, and use of data created. The community values the cooperation and roles played by academic partners and defines control as appropriate and beneficial to their culture and context.
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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 | 0.234 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.040 | 0.071 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".