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Record W1582849784

The Politics of Urban Cultural Policy: Global Perspectives

2012· book· en· W1582849784 on OpenAlexaff
Carl Grodach, Daniel Silver

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCultural policyCreative cityPoliticsUrban politicsThe artsCreative classCreative CitiesSociologyCapital (architecture)Public administrationPolitical sciencePolitical economyEconomic historyLawHistoryArtCreativityVisual arts
DOInot available

Abstract

fetched live from OpenAlex

The Politics of Urban Cultural Policy brings together a range of international experts to critically analyze the ways that governmental actors and non-governmental entities attempt to influence the production and implementation of urban policies directed at the arts, culture, and creative activity. Presenting a global set of case studies that span five continents and 22 cities, the essays in this book advance our understanding of how the dynamic interplay between economic and political context, institutional arrangements, and social networks affect urban cultural policy-making and the ways that these policies impact urban development and influence urban governance. The volume comparatively studies urban cultural policy-making in a diverse set of contexts, analyzes the positive and negative outcomes of policy for different constituencies, and identifies the most effective policy directions, emerging political challenges, and most promising opportunities for building effective cultural policy coalitions. The volume provides a comprehensive and in-depth engagement with the political process of urban cultural policy and urban development studies around the world. It will be of interest to students and researchers interested in urban planning, urban studies and cultural studies.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.486
Threshold uncertainty score0.997

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)0.0010.000

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 head0.043
GPT teacher head0.327
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations48
Published2012
Admission routes1
Has abstractyes

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