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Record W2074654807 · doi:10.1108/20442081211232990

Why promote sold‐out concerts? A Durkheimian analysis

2012· article· en· W2074654807 on OpenAlexaboutno aff
Mark Duffett

Bibliographic record

VenueArts Marketing An International Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMarketing buzzOriginalitySchema (genetic algorithms)Value (mathematics)SociologynobodyAdvertisingMarketingAestheticsQualitative researchBusinessSocial scienceComputer scienceArt

Abstract

fetched live from OpenAlex

Purpose The aim of this research paper is to examine why concert promoters sometimes advertise sold‐out live music shows when nobody can buy tickets any longer. Design/methodology/approach Durkheim's theory of religion as a thrilling social activity is used to hypothesize that the advertising of sold‐out events reminds audiences that star performers are popular and therefore helps to generate the “buzz” around them. Interviews with a series of promoters from the USA, UK and Canada revealed, however, that they see more immediate and mundane reasons for advertising sold‐out shows, including building the artist's career profile and training consumers to buy next time round. Findings It was found that promoters could also organize the sales and advertising process to bring sold‐out events into being. While their explanations diverged from a Durkheimian schema, the results of their actions did not. In effect they serendipitously did cultural work to further the Durkheimian process without being consciously concerned by it as an explanation of motives. Originality/value This paper suggests that the Durkheimian model illuminates a point of connection between commerce and affect in the reception of star performances. Further research on live music using the model as a hypothesis may therefore be useful.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.036
GPT teacher head0.350
Teacher spread0.314 · 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 designObservational
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

Citations8
Published2012
Admission routes1
Has abstractyes

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