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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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