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
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".