Sound and the healthy city
Bibliographic record
Abstract
At an international level it is recognised that urban noise has serious and negative public health impacts. This leading editorial and the special issue it accompanies seeks to broaden this agenda. An important goal for Cities & Health is to give ear to new urban health topics, methods and collaborations. In doing so this paper presents the topic of urban sound and health from several unique angles. At its core, we deliberately move the focus beyond noise levels, as measured by decibels, and harm to health through the stress of relentless background noise. Instead, we focus on the concept of soundscape, a more qualitatively nuanced research subject of enquiry. The paper serves as an introduction to soundscape and health from several distinct disciplinary positions and lays a good intellectual foundation for the twenty-two papers published in this special issue. We hope that through a soundscape approach we can encourage fresh thinking about urban sound, including how people perceive and relate to their sonic environments, and show how sound can contribute to health. We believe that this approach can provide a collaborative platform for sound artists, sound technologists, urbanists and local people to work together with public health and create healthier urban environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".