Reforming Statutory Public Hearings for Planning
Bibliographic record
Abstract
This paper considers the continued relevance of statutorily required public hearings as effective forums for participation in planning in Canada, and whether provincial and municipal governments should seek to reform or remove them from the planning process. We examine the entire rezoning and amendment process in four cities: Toronto and Brampton in Ontario, and Vancouver and Surrey in British Columbia, by drawing on the findings of our earlier study (Moore and Caporale 2023) and an additional 27 interviews we conducted with stakeholders familiar with the respective planning processes. Based on our analysis, we find that statutory public hearings are a necessary part of the planning process, but in their current form are ineffectual forums for public participation. We suggest several reforms to address their current failings.
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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 | 0.061 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".