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Record W3092626370 · doi:10.1080/10286632.2020.1811256

Cultural policies in cities of the ‘global South’: a multi-scalar approach

2020· article· en· W3092626370 on OpenAlexaff
Jérémie Molho, Peggy Levitt, Nick Dines, Anna Triandafyllidou

Bibliographic record

VenueInternational Journal of Cultural Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHegemonyNegotiationGentrificationCultural policyUrban politicsPoliticsSociologyPolitical economyPolitical scienceNarrativeSocial scienceEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

Building on the literature on global cities and on the worlding of cities, the articles in this special issue chart how cities outside Europe and North America try to reinvent and rescale themselves using culture. They suggest that the fabric of urban cultural policy is embedded in multi-scalar power dynamics. First, the contributions in this special issue reveal the importance of circulating standards across borders in structuring narratives about urban history, heritage and identity, in conjunction with local actors’ interests. Second, the diffusion of hegemonic cultural policy models such as the “creative city” leads to logics of exclusion, gentrification, and has been met with resistance, which suggest that these models can be to the detriment of local residents, despite the progressive values they are often claim to promote. Third, this special issue points to the need to rethink the politics of cultural policy mobility and offers conceptual tools such as vernacularization to make sense of the ways in which urban elites navigate, negotiate and take advantage of circulating cultural policy models.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.029
Scholarly communication0.0230.009
Open science0.0010.010
Research integrity0.0030.006
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 head0.104
GPT teacher head0.372
Teacher spread0.268 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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