Data Colonialism in Canada: Decolonizing Data Through Indigenous data governance
Bibliographic record
Abstract
The current states of First Nations, Inuit, and Métis data are not distinctive of the current moment but are linked to the historical operation of the colonial enterprise to denigrate and marginalize Indigenous peoples and knowledge through data colonialism. There is an international movement emerging that calls for Indigenous data governance (IDG) to resist and reconcile these colonial histories. In this context, this thesis is a study of how Indigenous data were constructed as colonial objects in Canada and what it means to decolonize these data and the practices which produce and govern them. It is argued that IDG provides the foundation for data decolonization in Canada, however this is fledgling work emerging at a critical juncture where there are ever-changing technological innovations, complex social issues, and legacies of colonial governance arrangements. This is important to consider as First Nations, Inuit, and Métis continue to assert sovereignty over their data.
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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 | 0.097 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".