The future of urban schools: integrating community into vertical school architecture
Bibliographic record
Abstract
Today we lack a substantial discussion on how educational design \nshould define our future. As Toronto continues to increase in density, so \nhas the demand for schools. Condo apartment completions averaged \napproximately 19,000 units per year up to 2020, which is in line with levels \nfrom 2014 to 20161 . If all the proposed residential units (active and built) \nare completed and registered, the number of registered condominium units \nin the City of Toronto will increase by 12% to a total of 210,350 units which \ncould generate approximately 20,000 new students by 20512 \n. School \nboards are struggling to keep up with demands for space. Traditionally \ndesigned in a horizontal, dispersed fashion, schools are meant to provide the \nspace to promote learning and growth, relying heavily on outdoor spaces. \nHowever, as the population within the city increases, land becomes scarce. \nFostering a learning environment that is not dependent on the restrictions \nof external physical environment becomes an ever-increasing challenge. \nThis project fuses urban design and architecture together. Urban \ndesign fails when the architecture is forgotten and vice versa. Researching \nboth the current urban design strategies and the current architectural design \ntypologies of the Yonge Eglinton area, my goal is to fuse the typologies \ntogether for the future development of the area. With all the development of \nthe Yonge Eglinton area, an ongoing trend is building vertically. This thesis \nexamines the current city density and explores the advantages of a newly \nimagined mid-town school. The traditional design of a school surrounded \nby green, open space and playgrounds may no longer be a reality for areas \nof Toronto that are densely populated with unsustainable infrastructure in \nexisting schools. Therefore, this proposal supports a vertical elementary \nschool typology with access to a variety of community services in the same \nbuilding. The selected services that the mixed use project offers to the \ncommunity were purposefully chosen to cultivate meaningful relationships \nbetween the surrounding neighbourhoods, parents, students and \nteachers, creating a thoughtful and useful building dedicated to serving \nthe Yonge and Eglinton community. With this new development model, the \ncommunity will be embedded into the school. As part of my site analysis, \nthe history and the future of the Yonge Eglinton community will be taken \ninto careful consideration in order to design a site-specific community hub.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".