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Record W2586930455 · doi:10.1111/1468-2427.12441

Governed Through Ghost Jurisdictions: Municipal Law, Inner Suburbs and Rooming Houses

2017· article· en· W2586930455 on OpenAlexaffabout
Lisa M. Freeman

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsJurisdictionCorporate governanceDowntownSpace (punctuation)Inner cityPovertyPolitical scienceSociologyLawPublic administrationGeographyBusinessRegional scienceFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.171
GPT teacher head0.457
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes2
Has abstractyes

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