Bibliographic record
Abstract
This article explores the concept of Indigenous Data Sovereignty (IDS) in Canada, examining its barriers, resources, implementation, and policy implications. While not an exhaustive list of all IDS-related policies, the article focuses on key definitions, successful implementations, support programs and resources, and outdated policies that hinder IDS and Indigenous governance practices. Through a First Nations lens, the paper highlights the importance of Indigenous People's control over data and knowledge about their communities and lands. It discusses the challenges of implementing IDS within non-Indigenous organizations and communities, including financial constraints and the influence of colonial policies. The article also addresses the impact of IDS on Indigenous self-determination, emphasizing the need for government and educational institutions to support IDS practices. Additionally, it explores the First Nations' principles of Ownership, Control, Access, and Possession (OCAP) as an example of successful IDS implementations. The paper acknowledges the role of data sovereignty in reconciliation frameworks and highlights resources such as the International Work Group for Indigenous Affairs (IWGIA) and the Global Indigenous Data Alliance (GIDA) that advocate for IDS and Indigenous self-governance. The conclusion emphasizes the ongoing need for support, collaboration, and the mobilization of UNDRIP and TRC frameworks to ensure the success of IDS and the amendment of colonial policies.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".