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Aestheticizing Space: Art, Gentrification and the City

2010· article· en· W1926952114 on OpenAlexaff
Vanessa Mathews

Bibliographic record

VenueGeography Compass · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGentrificationThe artsSociologyAgency (philosophy)ConceptualizationConversationSpace (punctuation)AestheticsVisual artsUrban spacePublic spaceMedia studiesSocial scienceArtCivil engineeringRegional scienceArchitectural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract This article explores the relationship between art and gentrification at the urban scale. In particular, it maps shifting conceptualizations of this relationship through a focus on art, artists and arts spaces in the successive waves of gentrification. The first half of the article outlines the conceptualization of the arts in the first and second waves of gentrification, beginning with artist location preferences, detailing how and why these areas become attractive for higher income groups, and the agency prescribed to artists within the process. In the second half, the article places these findings in conversation with current debates taking place in the field surrounding third wave gentrification, in particular, how the arts are incorporated into public‐policy and urban regeneration with a focus on public art and arts infrastructure. The conclusion raises questions about how the incorporation of the arts in city programming complicates understandings of gentrification, and presents future avenues for research.

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)0.001
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.045
Scholarly communication0.0110.003
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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

Citations156
Published2010
Admission routes1
Has abstractyes

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