An Exploratory Analysis of Infill Strategies for Achieving Higher Dwelling Densities in the Yellowbelt
Bibliographic record
Abstract
Toronto’s Yellowbelt, defined as areas zoned for predominantly single-detached housing typologies, is exclusionary and contributes to a housing supply shortage. This, in turn, increases the cost of housing. This exploratory study aims to simulate and examine the implications of higher-density infill in the Yellowbelt. This is achieved through the simulation of Edmonton, Ottawa, and Portland’s infill zoning reform, along with Toronto’s traditional R zoning, onto two neighbourhoods. The two neighbourhoods are zoned for single-detached dwellings, but reflect different urban fabrics. This research paper evaluates the tools used to control for density and explores their implications within the context of Toronto. This research concludes with a proposal to reconceptualize the Yellowbelt. Three categories are created to reflect three different opportunities for higher dwelling density infill, based on lot size. Zoning reform in the Yellowbelt will greatly increase Toronto’s housing supply, and may increase housing affordability depending on the specific types of regulation implemented.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".