Bibliographic record
Abstract
Local actors, including resident and business associations, do not simply influence decision-makers, but can also reshape the purportedly neutral governance model within which decision-making takes place. In big cities like Toronto, this reshaping exacerbates the existing geographic and socio-economic unevenness. The work of James Scott, Mariana Valverde, and Cheryl Teelucksingh helps to explain how local actors interface with seemingly neutral governance bodies to have their interests heard, particularly in relation to locally undesirable land uses. The paper considers two case studies detailing the governance practices at work in differing decisions about casinos in the City of Toronto. A 2012-2013 debate about a casino in downtown Toronto saw a little-used bylaw invoked by city councillors to help them investigate the effects of a casino on “local” issues like traffic and planning. These empowered local actors played a central role in the debate. By contrast, in a 2015 debate about a casino in a poor neighborhood on the margins of the city, the debate proceeded through the usual decision-making process for “city-wide” deliberations, leading to fewer opportunities for involvement by local actors. The final section brings theoretical literature and case studies together to conclude that the institutions of local governance can be reshaped depending on the local actors involved, and claims that shifts in scale, from local to city-wide, have implications for the inclusivity and fairness of Toronto’s governance model.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.064 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".