Canada’s Urban Indigenous Populations: Comparing Policy Learning in Winnipeg and Edmonton
Bibliographic record
Abstract
According to Census 2016 from Statistics Canada, Winnipeg and Edmonton have the largest Aboriginal populations among the census metropolitan areas (CMAs), which are areas with a total population of at least 100,000 people. Moreover, Aboriginal populations continues to grow in these metropolitan cities. However, city policies have not been adjusted accordingly to these changes, nor are they sufficient to address the Aboriginal community’s vulnerability especially regarding lower-cost housing. Exploring the condition of low-cost housing in the context of Winnipeg and Edmonton is essential due to the fact that this sector is directly influenced by the intersecting factors that make Aboriginal populations vulnerable. In addition to examining the condition of lower-cost housing, evidence of policy learning will also be analyzed. Policy learning involves evaluating past practices, recognize past policies, and is also a crucial part to avoiding failures in future policies. Unfortunately, it seems that for Winnipeg and Edmonton, it is not possible for authorities to address insufficient low-cost housing for the Aboriginal community through adequate 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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".