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Political Arts Urbanization: A Malaysian Case Study

2017· article· en· W2553944532 on OpenAlexaff
Sandra Smeltzer

Bibliographic record

VenueInternational Journal of Asian Social Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsWestern University
Fundersnot available
KeywordsPoliticsStatus quoSociologyMainstreamCreative classThe artsUrbanizationPolitical capitalDilemmaDemocracyCapital (architecture)Economic growthPolitical economyPublic relationsPolitical scienceEconomicsCreativityLawGeography

Abstract

fetched live from OpenAlex

This article explores politically oriented artistic production in Kuala Lumpur (KL), the capital city of Malaysia. Over the last several years, KL has become home to an expanding network of individuals who employ art as a vehicle to directly and indirectly challenge the country’s socio-political status quo and its ruling regime. Drawing on semi-structured interviews with a range of political artists living and working in the city, we find an aggregation of individuals generating similar but differentiated products with the overarching goal of advancing a more equitable, tolerant, and democratic society. We thus consider if this urban-based network represents an alternative way to mobilize the concept of a ‘cluster’ beyond mainstream academic and political narratives that emphasize the economic benefits accruing from developing a ‘creative’ class and workforce. We find that the process is not entirely different from what one might expect from traditional deployments of the concept of a cluster, but the objectives are different – rather than being principally geared towards financial gain, these individuals are primarily concerned with political objectives. We question, however, if this political arts urbanization can indeed be described as a ‘cluster’ given that, for a number of logistical and political reasons, the artists and their work are spread out over a sprawling capital city.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.072
GPT teacher head0.408
Teacher spread0.336 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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