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Record W4210547237 · doi:10.1177/00420980211066420

Fifty years of Business Improvement Districts: A reappraisal of the dominant perspectives and debates

2022· article· en· W4210547237 on OpenAlexaffabout
Daniel Kudla

Bibliographic record

VenueUrban Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsScholarshipCorporate governanceNeoliberalism (international relations)Work (physics)SociologyPolitical sciencePublic administrationPublic relationsPolitical economyEconomicsManagementEngineeringLaw

Abstract

fetched live from OpenAlex

Originally created in 1970 by a small group of business people in Toronto’s Bloor West Village, Business Improvement Districts (hereafter BIDs) have become commonplace urban revitalisation strategies in cities across the world. Many critical urban scholars have conceptualised BIDs as neoliberal organisations and have resultantly critiqued their role in contemporary urban governance. With BIDs now existing for over 50 years, the purpose of this paper is to provide an overdue reappraisal of the BID research and orient future scholarship. After describing key debates from early BID research, this paper analyses two distinct themes in more recent scholarship: (1) BID policy mobility, and (2) BIDs and social regulation. As the BID model has been transferred to new locations across both the Global North and South, its rapid mobility demonstrates the permeability, resilience and limits of neoliberal urban policies. Moreover, BIDs’ social control tactics highlight how these organisations are shaped by a neoliberal logic that seeks to manage and control urban spaces in ways that attract desirable consumers and exclude the visible poor. This paper outlines the origins of both bodies of work and traces common patterns and variances over time. It concludes by highlighting gaps in the existing literature and offers suggestions for future work.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.285
Teacher spread0.265 · 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

Citations26
Published2022
Admission routes2
Has abstractyes

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