Bibliographic record
Abstract
In the Toronto policy and planning community, Section 37 (S37) of the Planning Act, which allows for a form of “density for benefit agreement, ” is the source of much debate and disagreement. Based on data on the value, type, and location of S37 benefits over the period from 2007 to 2011, this paper identifies a number of trends. The benefits were heavily concentrated in the parts of the city that have experienced the housing boom, notably the downtown core. Developer contributions were largely split between cash and in-kind, and were allocated to a wide variety of public purposes within and across the City’s wards – mainly “desirable visual amenities” such as parks, roads and streetscapes, and public art. Finally, most benefits were close to the development, and they almost always remained within the ward. These findings suggest a few important conclusions. First, there is little certainty about what S37 benefits should be used for. In practice, the major rationale appears to be to compensate neighbouring residents for the “negative impacts” of the added density. Second, the inconsistent use of S37 benefits likely relates to the fact that agreements are negotiated on a case-by-case basis, with no established City practice for identifying what benefits to secure, and a great deal of discretion resting with ward councillors. Third, the inconsistent use of S37 could also result from the lack of clarity in provincial legislation and planning policies. Given the significant questions this paper raises about the use of S37s in Toronto, there should be serious consideration of whether to abolish, reform, or replace S37 with alternative tools, such as inclusionary housing policies or fixed charges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 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".