Exercising rights over data: a journey towards First Nations data sovereignty in Canada
Bibliographic record
Abstract
This article examines the journey towards First Nations data sovereignty in Canada, with a focus on the pivotal role of the First Nations Information Governance Centre (FNIGC) and its First Nations Data Governance Strategy (FNDGS). Framed within the context of Canada's commitment to reconciliation and recognition of Indigenous rights, the FNDGS represents a significant step towards self-determination through ownership and control of data. The article discusses the historical context of colonization and its impact on data governance capacity among First Nations, highlighting the emergence of the FNDGS as a transformative force. It explores the foundational principles of the FNDGS, emphasizing the importance of data sovereignty as a tool for empowerment and self-governance. The implementation phases of the FNDGS are outlined, showcasing a multi-phased approach that prioritizes data capacity building through community-driven and nation-based principles and approaches. Additionally, the commentary discusses the lessons learned from the COVID-19 pandemic and the imperative of Indigenous-led data strategies to mitigate the risks of a future pandemic. It concludes by reflecting on the prospects of the FNDGS and its critical role alongside Canada's continued commitment to Indigenous rights in realizing true data sovereignty for First Nations.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.050 | 0.035 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| 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; 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".