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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".