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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".