Bibliographic record
Abstract
For the past three decades, government investments in social and affordable housing in Canada have drastically declined. The Toronto housing market is increasingly expensive and the lower and middle classes struggle to afford quality housing that meets their diverse needs. The housing crisis is explained as an outcome of global and national neoliberalization trends; including the commodification of housing, the dismantling of social supports, the underfunding and discontinuity of social housing programs, and the decline of affordable rentals in Toronto’s housing stock. These structural issues require deep societal transformations and political commitments that are not likely to materialize in the near future. Hence, I argue that alternative housing methods and the strengths of all sectors should be leveraged in the meantime to incentivize and conserve quality affordable housing units. The goal of this Major Project is to, first, understand the current context in Toronto and affordable housing policies, programs, and tools, both current and historical. Second, the paper examines both domestic and international alternative affordable housing typologies, tenures, construction methods, policy models, and financing mechanisms which are underutilized or not used at all in Toronto. The third section dives deeper into opportunities for some of these unique housing methods to be implemented within Toronto, with the lens of legislative applicability and opportunities for implementation. The Major Paper explains why some of these models are underutilized or non-existent within Toronto’s housing ecosystem. It concludes that there is room for innovation in affordable housing, and that cities like Toronto should leverage all housing sectors to provide affordable housing that meets the diverse needs of all residents.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".