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Spatial Regulation, Dispersal, and the Aesthetics of the City: Conservation Officer Policing of Homeless People in Ottawa, Canada<sup>1</sup>

2011· article· en· W2115398507 on OpenAlexaffabout
Kevin Walby, Randy K. Lippert

Bibliographic record

VenueAntipode · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of WindsorUniversity of Victoria
Fundersnot available
KeywordsTemporalitiesCommissionBiological dispersalOfficerSociologyCapital (architecture)CriminologyPolitical scienceGeographyLawArchaeology

Abstract

fetched live from OpenAlex

Abstract: In this article, we examine the spatial regulation of homeless people by National Capital Commission (NCC) conservation officers in Canada's capital city, Ottawa. We explore NCC officer practices by analyzing occurrence reports obtained through access to information (ATI) requests and interview transcripts. We contend that policing of NCC parks is organized according to a logic of dispersal. Dispersal policing aims to preserve an aesthetic for public consumption and ceremonial nationalism, entails specific temporalities, and is actuated through a public/private policing network. We argue that “dispersal” more accurately conceptualizes the spatial regulation in this case compared with alternative concepts (ie banishment) and thus supplements existing typologies of spatial regulation. We conclude with a discussion of these typologies and of the worth of ATI for future research on urban policing and regulation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.009
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations80
Published2011
Admission routes2
Has abstractyes

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