The Many Faces of Leadership in a Thriving City: A Rethink of the Toronto Narrative
Bibliographic record
Abstract
For most of the last four years, Toronto has been fixated on Mayor Rob Ford. Why do these perceptions seem so disconnected from reality? Over the last decade, Toronto has risen in world rankings, placing in the top ten consistently in measures of livability, prosperity, and business investment attractiveness. It is clearly the number-one city in Canada in financial power and cultural facilities, and as a media centre. The fact is, Toronto is booming, and this hasn’t happened by accident. While the frantic media coverage has given the impression that the City suffers from leadership paralysis, the reality is very different. At City Hall, members of council and staff have done their utmost to fill the leadership vacuum. A less-recognized ingredient in Toronto’s success, however, has been the city-building and civic leadership that has emerged from vibrant and innovative private firms, public institutions, non-profits, and cultural sector organizations in Toronto’s wider civil society. There are many faces of leadership in a thriving city. This paper, the second in the IMFG’s PreElection series, profiles some of them and reflects on how, for any great city, the whole is greater than the sum of its parts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.030 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".