Local Government Response to Housing Unaffordability in Three Major Canadian Cities: A Study of Vancouver, Calgary, and Toronto
Bibliographic record
Abstract
In major cities globally, including those in Canada, many residents struggle to find affordable housing. Canadian municipalities have a growing role in responding to this issue. The objective of this paper is to investigate the municipal-level response to issues of housing affordability in three major Canadian cities: Vancouver, Calgary, and Toronto. Specifically, each city has committed to increasing the supply of affordable housing as one of its primary methods of responding to this issue. This paper provides an analysis and comparison of the goals set by each of the three case study municipalities and the real increases in affordable housing stock reported in the 2010s, with the finding that Vancouver has generally set the highest goals and made the largest increases to the stock of affordable housing. A discussion of major successful affordable housing initiatives in each municipality follows, namely Vancouver’s partnerships with other agencies to produce supportive housing, and use of modular housing as supportive and social housing; Calgary’s Resolve campaign to produce affordable housing, and Housing Incentive Program to incentivize the creation of new affordable rental housing; and Toronto’s partnerships with other agencies to produce supportive housing, and revitalization of Toronto Community Housing Corporation-owned social housing units. I find that it is partnerships with other actors, and especially the provincial government, that leads to the success of these initiatives in increasing the stock of affordable housing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".