Students in my backyard: locating areas for purpose-built student accommodations near campus
Bibliographic record
Abstract
The City of Toronto is home to four major universities and over 184,000 post-secondary students, most of whom will need a place to call home. It has become typical for students at urban universities to be housed on campus in student residences for first year, after which most students will seek accommodation in the neighbourhoods closest to campus. There are many factors affecting the ability for students to locate close to campus, of which affordability is at the forefront. The research of this paper is two-fold; locate areas close to each of Toronto’s four university campuses which may accommodate purpose-built student accommodations and refine these areas to identify areas where the development can be delivered as a mid-rise typology. Through intensification capacity modelling, underutilized sites within areas close to campus were identified for their suitability to respond to both city initiatives of providing student housing and finding the missing middle on housing density were identified. Identifying these sites allows for city planners and universities to anticipate the concentration of students in existing neighbourhoods and plan for the effects of ‘studentification’, both as a tension between students and neighbours and for the regenerative effects on the community. Key Words student housing, studentification, mid-rise housing, missing middle, intensification
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".