Toronto laneway housing: neighbourhood densification strategies
Bibliographic record
Abstract
The COVID-19 pandemic acted as a catalyst to bring \nthe unaffordable housing issue in Toronto to the foreground. However, this crisis has been developing for several decades, and it is caused by many factors. One of \nthe critical factors is the zoning by-law that is restricting \ndensity and maintains the Yellow Belt in Toronto. This \nrestriction has created a Missing Middle housing issue \nthat requires a multi-faceted solution to provide Torontonians with affordable housing. However, in the laneways \nof Toronto, some housing density can be added to the \nexisting housing fabric, and that is the Laneway House. \nThis thesis is focused on providing housing density to \nexisting laneways within the city, and proposes several \nimprovements on the current zoning by-law for laneway \nsuites implemented by the City of Toronto in 2018. The \nthesis argues that the existing regulations are deterring \nhomeowners from building laneway suites. With more \nflexible zoning by-laws and incentivizing homeowners, \nmore laneway suites can be constructed to increase the \nhousing density in Toronto and contribute positively to \nhousing affordability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".