Bibliographic record
Abstract
One of the many impacts of the Covid pandemic on Canadian cities was the complete collapse of short-term rental (STR) markets, as long-distance travel nearly vanished for more than a year. Many dedicated STRs shifted back to the long-term rental market, but others remained on STR platforms such as Airbnb but with minimum stays of one month or more—a land use we describe as “medium-term rentals” (MTRs). This paper provides a planning analysis of online-platform-mediated MTRs in Canadian cities and their housing-market, land-use, and regulatory implications. First, we identify and explore the regulatory grey zone inhabited by MTRs, which appear to be neither standard residential tenancies nor short-term tourist accommodations. Second, the paper provides a brief empirical overview of the emergence of MTRs during and after the Covid pandemic in Toronto, Montreal, and Vancouver. Third, the paper uses a policy case study of situations in which Ontario’s Landlord and Tenant Board has been asked to adjudicate non-standard tenancies to establish whether there is a planning basis for distinguishing medium-term rentals from other tenancy types. The paper concludes by identifying a key planning principle which could allow Canadian municipalities to pull MTRs out of the regulatory grey zone: regulating type of stay instead of length of stay.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".