Indigenous Data Matters: Finding Data for First Nations, Inuk and Metis Peoples in Canada
Bibliographic record
Abstract
Based on the work by three academic data professionals who created the Data on Racialized Populations in Canada guide, the presenters will go into more detail about finding data for First Nations, Inuk and Metis Peoples in Canada. The presentation will explore the historical nature of some Indigenous data sources with examples that will be provided of how the federal government of Canada has collected data on Indigenous peoples, often through a colonial lens. There will be a focus on how terminology necessary for searching may include language that can be problematic and/or offensive to contemporary users. Accordingly, the content will illustrate how the vocabulary used to refer to racial, ethnic, religious and cultural groups is specific to the time period when the data was collected and does not reflect the attitudes and viewpoints of contemporary society. More recent trends of inclusive terminology will also be explored and how this reaffirms Indigenous identity in the data. Finally, an overview of data sovereignty will end the presentation to allow insight into how data is collected, gives ownership and is used by Indigenous communities through relevant resources.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.029 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".