CAN ONTARIO'S HOUSING CRISIS BE FIXED? UNDERSTANDING THE ORIGINS AND PROPOSED SOLUTIONS TO AN ONGOING CRISIS IN HOUSING AFFORDABILITY.
Bibliographic record
Abstract
In the 15 years before the First World War, Toronto's population increased quickly by over 140 percent and the city annexed significant amounts of land to keep up with suburban growth. This period marked the beginning of the housing challenges in Toronto (Purdy, 2003). Stagnant real incomes, the delayed expansion of the streetcar system, and rising residential densities all contributed to Toronto's housing crisis during this period of reforms. Since then, the size of Toronto’s population increased from 470,000 before the First World War to 6,372,000 in 2023 and the housing crisis has only gotten worse, with 1 in 5 households in the GTA living in core housing need (Fung et al., 2023). Considering the recent housing problems reported in the GTA, the question is how the current housing crisis has been impacted by the historical context of Toronto’s urban development as well as ongoing economic and social problems. To answer this question, this paper seeks to critically examine the multiple aspects of housing affordability in GTA. It will analyze the main factors contributing to the affordability crisis in the region and further delve into the daunting barriers that Ontario residents, including homeless individuals, encounter while attempting to access affordable housing in the GTA It will also explore various recommendations and solutions that have emerged in the quest to tackle the housing crisis in GTA. While these solutions contribute to mitigating the housing crisis, it is imperative for the government to collaborate with agencies and stakeholders to diversify the economy and allocate additional resources for job creation. This comprehensive approach, incorporating the benefits of proposed policies while addressing their flaws, is crucial for developing long-term and equitable solutions as the GTA navigates its housing crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".