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Record W4321091818 · doi:10.1080/14649365.2023.2177717

Masking Visible Poverty through ‘Activation’: Creative Placemaking as a Compassionate Revanchist Policy

2023· article· en· W4321091818 on OpenAlexaffabout
Daniel Kudla

Bibliographic record

VenueSocial & Cultural Geography · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPlacemakingDowntownPanacea (medicine)SociologyPovertyUrban planningPublic administrationPublic relationsPolitical scienceUrban designLawCivil engineeringHistoryEngineeringArchaeology

Abstract

fetched live from OpenAlex

Creative placemaking strategies are widely adopted by urban planners, local governments, and business communities in hopes to revive economically struggling urban areas. These strategies seek to attract pedestrian traffic by facilitating arts and cultural activities in underutilized urban spaces. While these are seemingly innocuous and uncontroversial urban design strategies, I argue that a particular creative placemaking tactic called ‘activation’ is a compassionate revanchist policy that is touted as a caring approach but, in practice, functions to mask visible poverty to advance a capital project in a revitalizing urban space. Drawing from a case study of a downtown revitalization project in London, Ontario (Canada), I show how a private placemaking consultant narrates and legitimizes this policy to city councillors as well as how the downtown business association rationalizes and enacts activation strategies. This demonstrates that private placemaking consultants are powerful actors who transfer and legitimize simplistic spatial solutions as a panacea to a local ‘urban crisis’. Activation does not resemble punitive tactics that exclude and criminalize homelessness, it rather aims to dissolve the homeless within the fabric of the revitalizing urban environment.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.368
Teacher spread0.332 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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