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“White Night”: Gentrification, Racial Exclusion, and Perceptions and Participation in the Arts

2011· article· en· W2039249605 on OpenAlexaboutno aff
Samuel Shaw, Daniel Monroe Sullivan

Bibliographic record

VenueCity and Community · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
FundersVanderbilt UniversityNational Science Foundation
KeywordsGentrificationThe artsSociologyPerceptionWhite (mutation)Race (biology)ThursdayGender studiesVisual artsPsychologyEconomic growthArt

Abstract

fetched live from OpenAlex

Art festivals are a feature of many urban districts undergoing gentrification; they help to catalyze change by drawing a set of consumers with particular cultural interests. This article examines whether the arts produce racial exclusions by examining long–term Black and White residents’ participation in and perceptions of the monthly Last Thursday Art Walks in Portland's gentrifying Alberta Arts District. We use surveys to measure arts participation and follow–up, in–depth interviews to understand whether long–time residents feel excluded by the arts, and if race is a factor. We find that Black residents participate less in Last Thursdays than White residents, and they often feel uncomfortable or unwelcome. We conclude that the arts–anchored symbolic economy results in racial exclusions that have little to do with differences in arts appreciation, but much to do with perceptions of people associated with the arts, and with residents’ abilities to use the arts to identify with neighborhood changes.

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: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.512

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 studies0.0040.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.340
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

Citations109
Published2011
Admission routes1
Has abstractyes

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