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Record W2331154048 · doi:10.1386/jucs.1.2.235_1

Richard Price’s Lower East Side: Cops, culture and gentrification

2014· article· en· W2331154048 on OpenAlexaff
Thomas Heise

Bibliographic record

VenueJournal of Urban Cultural Studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsMcGill University
Fundersnot available
KeywordsGentrificationSpeculationSociologyRedevelopmentEthnic groupNarrativeAestheticsNeighbourhood (mathematics)HistoryPolitical scienceAnthropologyArtLawLiteratureEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract This article interrogates the dominant cultural narrative of gentrification and its deployment and resignification by Richard Price’s novel Lush Life (2008), set in the historic immigrant neighbourhood of the Lower East Side in 2002. Drawing upon theories of urban development and urban history by Neil Smith, Liz Bondi, Christopher Mele and Richard Lloyd, this article argues that Lush Life (2008) dramatizes the violent underpinnings of gentrification. At the same time, Price’s text ironizes contemporary urban redevelopment strategies that resignify and market gritty subcultural and ethnic differences as style in the service of real-estate speculation. What Price’s novel endeavours to show is that in the midst of disorienting social and physical change, urban subjects construct psychogeographies that reinforce personal and social boundaries. They gravitate towards residual signifiers of history and ethnic identification, which capitalist development itself unearths and reanimates, believing they might hold the key to establishing a stabilizing geographical rootedness at the very moment that dominant cultural and physical meanings of place are being upended.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.022
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.257
Teacher spread0.215 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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