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Record W4409384645 · doi:10.1016/j.ccs.2025.100634

Creative improvement, cultural infrastructure and urban zones: a tale of three cities and their cultural districts

2025· article· en· W4409384645 on OpenAlexaboutno aff
Abigail Gilmore, Claire Burnill-Maier

Bibliographic record

VenueCity Culture and Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
FundersUniversity of Manchester
KeywordsEconomic geographyGeographyEconomic growthSociologyRegional scienceEconomics

Abstract

fetched live from OpenAlex

This paper critically examines place governance and cultural policy decision-making in the context of three cities - Melbourne, Manchester and Toronto. It takes an infrastructural lens to examine the narrative histories and policy rationales for creating spatial zones in which culture is demarcated as an engine for social, cultural and economic development in each of these cities. By contextualising each city in relation to their individual histories, social, economic and political dimensions, the paper offers insights into the global similarities and differences in cultural district development, exposing a reliance on interests and actors that extend far beyond the state. It finds that the zoning of cultural space intermediates these interests requiring balance and oversight to ensure democratic participation and urban justice. • The paper offers a comparative analysis of three cases of cultural ‘zoning’ in city-regions that have dominant ‘creative city’ narratives. • It is based on empirical research including semi-structured interviews, ethnography and policy analysis. • Literature review produces interdisciplinary analytical frame using concepts such as ‘extrastatecraft’, ‘zoning’ and place governance’. • It moves beyond arguments concerning the success or otherwise of cultural districts to explore the spatial dynamics of governance and statecraft.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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.

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

Citations9
Published2025
Admission routes1
Has abstractyes

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