Evidential approach to affordable housing programme development: an Ontario case study
Bibliographic record
Abstract
Housing affordability is a global crisis. UN Habitat estimated in 2020 that 80% of global cities do not have affordable housing options for the majority of their population; the World Economic Forum estimates a need for an extra two billion homes over the next 75 years (96,000 completions per day). The Canadian Centre for Housing Rights points to a confluence of economic, social and governmental factors created today’s homelessness and affordability issues. In Q3 2022, The Royal Bank of Canada reported that 62.7% of Ontarians' household income goes towards the cost of home ownership, skewed by 85.2% in Toronto. Traditional affordable housing models aren’t working. A 2023 Ontario regional affordable housing feasibility study provided the opportunity for a new approach. Independent analysis of open-source evidence, with risk evaluation and allocation, demonstrated the local applicability of two decades of international lessons of affordable housing projects. It provided an objective approach that serves the health of tenants and the wider community. In challenging preconceptions, it showed that a capability-based infrastructure development planning approach would be most suitable for affordable housing projects. Furthermore, it found that an enterprise-wide, transparent and outcomes-focussed governance structure is essential to successful risk allocation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".