(Re)creating Boundary Lines: Assessing Toronto's Ward Boundary Review Process
Bibliographic record
Abstract
When Toronto's Ward Boundary Review (WBR) began in 2013, the city's 44 wards varied widely in size, ranging from 45, 000 to 90, 000 residents. The WBR’s multi-year process was designed by staff and led by consultants, with ample opportunity for involvement by councillors. The final ward boundaries were approved in November 2016 without significant deviation from those recommended in the consultants’ report. The result was the addition of three new wards. Assuming there are no successful appeals of the decision, the new ward boundaries will be in place for the 2018 election. The ward boundary review raised significant questions about the regularity by which such reviews should be held, the role of city councillors as participants and decision-makers in the process, and the relationship between the WBR and a future governance review. This paper sets out the contested legal terrain within which the City of Toronto’s WBR took place and assesses possible next steps, including the grounds for a possible Ontario Municipal Board appeal. Ultimately, the paper concludes that wards are but one important component of municipal representation and governance.
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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 | 0.117 | 0.435 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 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".