Community-Led Transformation of the Housing and Education Systems by York Factory First Nation, Manitoba, Canada
Bibliographic record
Abstract
First Nation communities in Canada face systemic barriers to quality housing and education. This participatory research with Indigenous people from York Factory First Nation (YFFN) from 2022 to 2025 focused on youth capacity-building and developing funding proposals. The YFFN partnered with the University of Manitoba to compete successfully for a “rural, rapid housing” $8.4 million grant in 2023-2024 to fund infrastructure for the education and housing ecosystem of YFFN. This grant funded a state-of-the-art trades school building, four prototype housing designs, dormitory facilities and a Wikiwin post-secondary education program. The Wikiwin “earn as you learn” program offered 20 YFFN youth paid internships to build YFFN houses and take university courses in their community. To analyze the sustainable livelihood benefits of the Wikiwin program, all 20 Wikiwin students were surveyed when starting in 2023 or 2024, and again in 2025, with an 85% response rate (n = 17). A paired t-test found statistically significant (p p < 0.05) for many human, financial, and social assets and at higher rates than the YFFN control group (n = 9). This case study found many benefits of community-led post-secondary education, offering a promising approach to transform education and housing systems in First Nation communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".