Disturbing the Balance: Exploring the Implications of Employment Land Conversions in the City of Toronto
Bibliographic record
Abstract
The aim of this paper is to explore and understand the implications of employment land conversions in the City of Toronto, and in particular its impact on housing affordability. The municipal government has attempted to remedy the issue of land conversions by introducing new policies that are meant to restore, or at the very least slow down, the loss of employment opportunities through the protection of employment lands. The paper analyzes the new employment land policies within the context of current and outstanding development applications seeking land conversions to permit residential uses. The purpose of examining employment land conversions as it relates to housing affordability is to understand why residential development may not improve housing security. Although landowners and developers leverage the language on affordability, transit supportive development, and creating employment opportunities to legitimize the conversion requests, the redesignation of employment land to permit residential uses does not necessarily advance these goals. Rather, the conversions can fuel a series of other processes such as gentrification and the loss of good job that may make housing in the City less, rather than more, affordable. Safeguarding good jobs can generally improve housing security for a greater proportion of urban residents. I ask, however, whether the municipal government's emphasis on the protection of employment lands alone is enough and if this strategy should be coupled with a series of other policies to improve the economic circumstances of city residents?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".