Re-territorializing democracy: Social service regionalism and ‘recalibrated’ governance in Ontario’s Peel Region
Bibliographic record
Abstract
In 2023-2024, the Ontario government proposed dissolving Peel Region, a two-tier governance structure serving one of Canada’s most diverse and rapidly urbanizing suburban areas. While framed as technocratic restructuring, the initiative was underpinned by political calculation and populist appeals to local autonomy, revealing deeper tensions in how territory, governance and social service delivery are imagined and politicized. Approaching territory as a relational and contested construct—an active force in decision-making and a site of social relations imbued with meaning—the article critically examines the political stage surrounding the proposed dissolution and foregrounds how a network of non-profit social service providers, the Metamorphosis Network of Peel, became a key territorial actor resisting top-down reforms. Although the dissolution plan was ultimately abandoned, it exposed the fragility of state-led governance and opened space for alternative imaginaries and re-territorialization grounded in networks of care infrastructure and embedded forms of expertise. Tracing how Metamorphosis mobilized place-based advocacy and service-oriented networks, we argue that this coordinated response, while not municipalist in a classical or insurgent sense, reflects an incipient form of regionalist praxis that we call here “social service regionalism.” The Peel case contributes to international debates on re-territorialization by advancing a processual, post-sovereigntist account of spatial politics. It illustrates how regional governance is recalibrated in practice not through bureaucratic engineering but through polycentric spatial politics and embedded networks more attuned to local realities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".