Strange Multiplicity: Diverse Patterns of Governance for Canadian Metropolitan Areas
Bibliographic record
Abstract
Abstract This chapter relates two scales of governance and political representation that are inextricable, but which are rarely considered together: the metropolitan and the municipal. The contemporary effectiveness and legitimacy of local governance, and the capacity of local institutions to articulate conflict, depends in large part on how these inherited institutions are “wired up”, and how contemporary actors use them. This chapter provides a window on the wide variation in institutional arrangements found in Canada, which has been enabled by federalism (provincial jurisdiction over local government), fiscal decentralization, and the non-integration of local politics with provincial and federal party systems. Canada’s decentralized federalism and dispersed urban system have enabled considerable institutional experimentation since World War II, especially over the past 25 years. The result is a differentiated patchwork of institutional structures and models. This variation is illustrated first through descriptive analyses of the relative institutional consolidation of local governance and intensity of political representation in Canada’s 41 metropolitan areas, and then through a comparison of Toronto and Montréal’s very different coordinating and representational structures. Convergence on a single model is unlikely to occur; rather, experimentation will continue in ways that defy easy generalization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".