Good building practice guidelines for healthy and sustainable First Nations housing in Canada
Bibliographic record
Abstract
The current state of First Nations housing in Canada is, in general, considered to be in poor condition, with a high portion of homes in need of major repairs. First Nations communities are facing inadequate and overcrowded living conditions which have been associated with increased health risks. Currently, there are no standardized methods, or accepted protocols for completing housing condition assessments in First Nations communities. Understanding the specific housing conditions and the associated health and safety risks is essential to developing effective strategies to improve the health, safety, durability and sustainability of the housing stock. In this thesis, a process for assessing housing conditions in a First Nations community is presented. Using a case study method, which includes on-site building condition assessments, inspections for visible mould, and mould air sampling of 159 homes at Roseau River Anishinabe First Nation in Southern Manitoba, as well as an occupant survey of 133 heads of household, 72 common housing deficiencies were identified and linked to 12 health and safety hazards that are associated with each of these deficiencies. This research study, which led to the development of a Good Building Practice (GBP) guide, fills a critical void in the current research by providing a link between specific housing conditions and deficiencies present. Understanding how housing conditions affect the health and safety of the occupants can be a powerful tool for determining which housing repairs are critical and for establishing a timeframe for the implementation of repairs and upgrades. The significance of this study is that it provides First Nations housing authorities with a tool to identify housing deficiencies that are common in their communities, allowing these authorities to allocate appropriate funding to address the most critical safety items first, and plan capital funding to schedule less urgent repairs. The GBP guidelines provide general recommendations for remediating each deficiency using specific objective-based goals including improved health, safety, durability, and functionality. These guidelines can also be used by housing designers as a tool to better understand some of the unique conditions and deficiencies present in First Nations housing so that they can proactively design systems that will address these common deficiencies before they occur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".