The Making of Music Venues: Inquiries into Global Urban History
Bibliographic record
Abstract
Music venues form nodal points around which to explore networks of people and the materialization of styles, trends, and ideas. Moreover, they work as condensers, bringing together the domain of aesthetic experience with social, political, and economic factors. Our special section takes these observations as a starting point to study these particular urban spaces, with a special focus on the first half of the long twentieth century. By highlighting agency, the interplay between music and architecture, and the human experience and use of music venues, we will address the making of such venues and their entanglement with urban cultures. Examples include a wide range of venues that provided high culture as well as mass entertainment in Nashville, Montreal, Ankara, and Havana. On a methodological level, the section broadens the field of urban history and at the same time points to some empirical limits of a global urban history. The focus on music venues thus serves as a means to inquire into the transformations of taste, experience, agency, and style in urban cultures, which, in some respects, withstand a “global” explanation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".