Housing challenges, mid-sized cities and the COVID-19 pandemic
Bibliographic record
Abstract
This article examines key housing challenges in mid-sized cities during the COVID-19 pandemic. Two questions guide our critical reflection: understanding to what extent the pandemic represents new challenges and what planners can do to respond to them? We use the example of the Region of Waterloo, situated 100km west of Toronto and one of Canada’s fastest growing urban areas. Waterloo has many similar characteristics to other mid-sized cities within commuting distance of large urban regions. In this article, we focus on two of the biggest (and inter-related) housing issues: inward migration from the Toronto Region and growing unaffordability. Both these challenges long-predate the COVID-19 pandemic, but there are early indicators that they are accelerating because of it. By rooting the challenges of the pandemic within longer trends and trajectories, our critical reflection suggests that many solutions that have long been understood to address housing inequalities are still important during the pandemic. Rather than devising new solutions, we argue that the pandemic requires implementing ideas called upon for years by researchers and advocates and more proactive planning to address market deficiencies.
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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 | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".