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Record W3103009293 · doi:10.1080/23800127.2020.1827516

“There are no days off, just days without shows”: precarious mobilities in the touring music industry

2020· article· en· W3103009293 on OpenAlexaff
Adam Zendel

Bibliographic record

VenueApplied Mobilities · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMobilitiesArtVisual artsArt historySociologySocial science

Abstract

fetched live from OpenAlex

The proliferation of online streaming music has led to a loss of income from the sale of recorded music pressuring artists and technicians to find alternate avenues to earn income, particularly through touring. Yet, there is little study on the lives and experiences of workers who tour. I use Lefebvre’s rhythmanalysis to analyze how the rhythm of “tour life” exposes workers to vulnerability and risk. Touring requires synchronizing the needs of daily life to an extreme form of employment-related geographical mobility. On tour, artists and workers struggle with basic self-care, eating, and sleeping in the context of constant travel. The rhythm of touring forces workers to be “always on” and always away in architectures that blur living and working space. The tour bus is a liminal space – neither home or worksite – yet features elements of both. Cultural work further produces an arrhythmic arrangement through the absorption of work into leisure time. For the public, travel or attending concerts are a form of leisure, the eurythmic counterpoint to their daily routines. For musicians and crew these are the quotidian elements of work. Working in the live music industry is seasonal, contractual and contingent on numerous cultural and economic forces. While touring music workers are an extreme case of employment related mobility, the rhythms of their working lives offer insight into the risk and vulnerability experienced by an increasingly mobile workforce.

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)0.001
metaresearch head score (Gemma)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.273
Teacher spread0.199 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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