Governed Through Ghost Jurisdictions: Municipal Law, Inner Suburbs and Rooming Houses
Bibliographic record
Abstract
Abstract This article examines the legal geography of municipal bylaws regulating rooming houses in the City of Toronto. Using a legal geography analysis of Toronto's rooming house licensing bylaw, I argue that this bylaw is a ghost jurisdiction that designates part of the city as illegal and has implications for governance of the inner suburbs. In so doing, I push the debate on legal geography forward by suggesting that we, as urban scholars, take the temporal seriously in our analysis of space. Drawing from semi‐structured interviews, archival data and participant observation, I analyse seemingly mundane legal mechanisms through the case study of suburban rooming houses. Overall, in this article I make three contributions. First, I demonstrate how a temporal analysis is important to legal geography inquiries of uneven regulation and spaces of poverty. Second, I suggest that studies of legal governance are integral for redefining suburban governance amidst socio‐economic decline in the inner suburbs. Third, I argue that studying urban legal mechanisms in the suburbs is essential for moving beyond downtown analytical frameworks and is needed to address how low‐income suburban tenants, a large majority of whom are racialized newcomers, are unevenly regulated and unfairly governed by local government.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".