Housing challenges, mid-sized cities and the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it