Alternatives for Greenbelt Development: A Comparative Study in Urban Planning
Bibliographic record
Abstract
This project examines Ontario Premier Doug Ford's plan to remove sections of the Greenbelt and develop them in order to build houses. The project argues that this proposal will not result in the construction of sufficient houses to resolve the housing crisis and will be detrimental to the environment. Through a comparative analysis of other cities across North America, the project argues that the solution to the housing crisis in Ontario instead lies in reforming municipal zoning policies. Governments need to reorient housing policy away from the existing emphasis on single-family residences. Zoning policies that allow for upward development rather than outward sprawl create high density neighborhoods which feature affordable housing alternatives like multiplexes and townhouses. The Ontario government should focus on investing in this form of development, and support non-profit organizations which aim to do the same. Promoting change to existing zoning bylaws will allow for the construction of more affordable housing alternatives, and thus create more compact urban areas. As a consequence, critical land for the environment, such as the Greenbelt will not be needlessly removed for the creation of low-density neighborhoods filled with single-family homes.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".