Local Scenes, Conditions of Music Making and Neoliberal City Management - A Case Study of Hamburg, Germany
Bibliographic record
Abstract
In recent years, city governments turned into restructuring of urban social and economic conditions and discovered ‘urban music’ as a way to ‘sell’ their ideas of creative (neo-liberal) city- development. Consequences of these strategies are, for example music related city marketing, creative industries support and spectacular flagship-projects like opera houses and concert halls. In contrast to these kinds of top-down planning, local scenes as well as bottom-up movements as breeding grounds of cultural production are obviously out of sight. But actually, effects of accelerated gentrification, restructuring of ‘creative’-quarters and the privatization of urban space seem to increase hindrances of urban musical/artistic production and the development of local scenes. In this context, the project examines how local scenes and conditions of music production are affected by ongoing changes in urban areas, and which effects of city policies and interventions can be identified on the individual level of scene players and institutions. Therefore, the case of Hamburg delivers a blueprint of what can be called a neo-liberal (‘creative’) city - including strong top-down planning one the one and struggeling bottom-up scenes and social/cultural initiatives on the other. Basing on empiric data, the ongoing research takes into account current conventions of music making as well as developments of urban scenes caused by strategies and interventions of local (cultural) politics. On a rather actor-centered level, the project examines existing gaps between urban planning/ city politics and the musical sector and discusses implications on the relationship between cultural actors and ‘their’ urban environment.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".