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Record W2862832735 · doi:10.1177/0308518x18786966

Managing territorial stigmatization from the ‘middle’: The revitalization of a post-industrial Business Improvement Area

2018· article· en· W2862832735 on OpenAlexaffabout
Daniel Kudla, Michael Courey

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsWestern UniversityUniversity of Guelph
Fundersnot available
KeywordsNegotiationConceptualizationNeighbourhood (mathematics)PoliticsSociologyStigma (botany)Political scienceSocial sciencePsychologyLaw

Abstract

fetched live from OpenAlex

Wacquant’s territorial stigmatization concept asserts that state/private actors mobilize discourses of stigmatization about specific areas in a city in order to legitimize spatial solutions in an attempt to solve complex political-economic problems. Unlike conventional studies of territorial stigmatization which delineate the concept between the production of stigma from ‘above’ and the resistance of stigma from ‘below,’ this paper contributes the concept of territorial stigmatization from the ‘middle’. Given their conceptualization as key players in the urban assemblage, we specifically examine how Business Improvement Areas (also known as Business Improvement Districts in the U.S) negotiate territorial stigmatization throughout the neighbourhood revitalization process. We highlight Business Improvement Areas’ unique middle position by drawing on data collected from interviews, media articles, and urban planning reports in London Ontario’s Old East Village over a fifteen-year period. In short, we find the use of territorial stigmatization by Business Improvement Areas is contingent upon their relationship within the urban assemblage (both actors from above and below).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.279
Teacher spread0.228 · 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.

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

Citations40
Published2018
Admission routes2
Has abstractyes

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