Statistics Canada Data Collection on Inuit: The Importance of Consultation and Context
Bibliographic record
Abstract
The Truth and Reconciliation Commission underlined the importance of knowing the history and context of Indigenous peoples in Canada, and how today’s institutions continue to be shaped by this legacy with detrimental effects. This article applies that lens to Statistics Canada data collection and reporting on Indigenous groups, particularly Inuit. Statistics Canada has already made strides in terms of consultations with national Indigenous organizations, but improvements can be made in more accurately capturing and describing Indigenous populations. These include: reviewing the definition of Métis, developing methods to include reserves and institutions (“collective dwellings”) fully, outreach strategies and methodologies to include transient and homeless persons, and providing both past and present contextual information to understand better the data and to avoid inadvertently contributing to stereotypes.
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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 | 0.226 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.032 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| 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".