Live Music, Intercity Competition, and Reputational Rents: Austin, Texas the ‘Live Music Capital of the World’
Bibliographic record
Abstract
In this paper I draw on a body of scholarship that focuses on how a central feature of capitalist urbanization is the willingness of firms to participate in a form of rent-seeking that exploits geographical differences. I then extend this analysis to the cultural economy. I use as my case study Austin, Texas, which since 1991 has branded itself the “Live Music Capital of the World.” The existing literature on Austin's urban entrepreneurial strategy, reflecting the dominant trends in urban and economic geography, focuses on how this branding campaign cultivated and exploited the geographical particularities of the city's cultural infrastructure. However, I contend that the changes brought about within the music industry influenced the success of this effort. In particular, I argue that the effectiveness of this branding effort is related to the changing value of live music within the music industry and especially the elevated position of music promoters (those firms that rely on live music as an essential part of their business). As this paper shows, the value of the city's branding efforts is related to the industrial success of two of the music industry's mid-sized promotional firms, SXSW Inc. and C3 Presents. These two Austin-based firms trade on its live-music brand but also, perhaps unwittingly, receive an extra-economic benefit that amplifies this reputation. In particular, I will focus on how a special music event, SXSW, and a music festival, Austin City Limits, help reinforce the image that has been enhanced by the city's branding efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".