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Record W4396507419 · doi:10.1080/09548963.2024.2345836

Cultural branding of cities: the role of live music in building a city’s brand

2024· article· en· W4396507419 on OpenAlexafffundabout
Alessandra Baiocchi, Danilo C. Dantas, Luís Alexandre Grubits de Paula Pessôa, François Colbert

Bibliographic record

VenueCultural Trends · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsHEC Montréal
FundersGlobal Affairs CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPlace brandingAdvertisingNation brandingBusinessPolitical sciencePublic relationsTourism

Abstract

fetched live from OpenAlex

This research explores the role of live music infrastructure and music cultural identity in building a city’s brand through a multiple case study comparing the cities of Rio de Janeiro, Brazil and Montreal, Canada. We conducted 60 interviews and observed 35 live music events in cities known for their strong live music traditions. The results show a relationship between live music infrastructure and identity that affects city branding. Rio’s music identity has developed from grassroots, while Montreal’s is shaped by government-funded efforts. Our findings suggest that a favorable infrastructure and environment can contribute to developing a city’s identity over time. Our study also highlights that when a city’s musical identity is strong, live music can survive in the city’s neighborhoods despite poor infrastructure. We discuss implications for academics, urban planners, and city branding professionals.

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)0.002
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: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0080.003
Open science0.0010.005
Research integrity0.0010.001
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 head0.070
GPT teacher head0.332
Teacher spread0.262 · 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

Citations6
Published2024
Admission routes3
Has abstractyes

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