Urban Music Governance : What Busking Can Teach Us about Data, Policy and Our Cities
Bibliographic record
Abstract
What happens when precarious urban cultural labourers take data collection, laws and policymaking into their own hands? Buskers have been part of our cities for hundreds of years, but they remain invisible to governments and in datasets. From nuisance to public art, this cultural practice can help us understand the politics of data collection, archives, regulatory frameworks and urban planning. Busking also responds to underlying questions on the boundaries of the right to the city – and who has a voice in shaping how our cities are planned and governed. A transnational exploration of street performance, Urban Music Governance examines the intricate limits of legality, data visibility and resistance from the perspective of those working at the social and regulatory margins of society. Based on a decade of fieldwork in Rio de Janeiro and Montreal, this book puts forward a lively account on why such an often-overlooked practice mattes today. By investigating the role of busking in contemporary society, Urban Music Governance presents an original interdisciplinary study that exposes how power dynamics in policymaking decide issues of access – and exclusion – around us, above and below ground. "
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".