Constructing the ideal soundscape: a practical study on closing the gaps between soundscape and urban designers
Bibliographic record
Abstract
Calls are increasingly made for an urban land-use policy that takes non-vision sensory modalities into account, like hearing, but agents capable of making such changes often lack the expertise to do so. The best progress in acoustics so far has been through intentional soundscape design, which considers sound during the urban design process rather than after. Indeed, soundscape designers should understand how complicated factors play out in-situ, such as findings linking increased driving speeds with acoustically treated roads. Armed with this knowledge, they can take action to prevent further harm to the urban landscape. In practice, however, what can happens is 1) papers in soundscape are written in language not interesting to urban designers; 2) research studies examine the current environment without proposing design updates; and 3) different investigators fail to agree on what constitutes wanted and unwanted noise. Each of these shortcomings contributes to a built environment that reflects little of our sophisticated understanding. In response, this presentation will: demonstrate how soundscape research can fit into current urban design frameworks; review the literature to suggest some small and large acoustically-optimized urban designs; and encourage collaboration channels for the direct flow of soundscape research into urban design practice.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".