“There are no days off, just days without shows”: precarious mobilities in the touring music industry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".