Strong(er) Mayors in Ontario – What Difference Will They Make?
Bibliographic record
Abstract
On October 19, 2022, IMFG convened a public panel discussion titled “Strong(er) Mayors – What Difference Will They Make?” The speakers were Karen Chapple, director of the School of Cities at the University of Toronto; City Hall journalist Matt Elliott; Alison Smith, assistant professor of political science at the University of Toronto; and Gabriel Eidelman, assistant professor, teaching stream, at the Munk School of Global Affairs and Public Policy at the University of Toronto. The panel was moderated by Zack Taylor, associate professor of political science at Western University. The discussion and follow-up questions by the audience brought to the surface a variety of perspectives, both for and against the “strong mayor” provisions of the Strong Mayors, Building Homes Act passed by the Ontario legislature on September 8, 2022. This commentary contextualizes and summarizes the speakers’ remarks. It also takes account of the additional provisions in the Better Municipal Governance Act passed on December 8, 2022, and the February 17, 2023, resignation of Mayor John Tory. The invited speakers provided insights on specific aspects of the law and their implications. Karen Chapple discussed the inspiration for the reform, American “strong mayor” cities. Matt Elliot probed how the relationship between the mayor and councillors might change. Alison Smith talked about the provincial-municipal intergovernmental relationship and the politics of housing policy. Finally, Gabriel Eidelman examines the implications of the change for the relationship between elected officials and professional administrative staff. Zack Taylor provides context for the discussion and, in his conclusion, addresses questions such as the risk of politicizing the public service, the implications for small and regional municipalities, and the role of the province.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".