Designing Sound for Public Spaces Through a Research-Creation Collaboration Framework
Bibliographic record
Abstract
When designing a sound installation in public spaces, creators consider a wide range of factors related to the site where it will be deployed as part of the artistic statement. However, anticipating the impact of the sound installation on user experience is difficult in the absence of established methods to inform the design and evaluate the outcomes. Based on three case studies involving sound artists and soundscape researchers, we propose a research-creation collaboration framework through four stages: 1) field recordings of pre-existing sound environments; 2) diagnosis of pre-existing sound environments and public space usage; 3) sound installation prototyping in laboratory settings; 4) evaluation after deployment. These stages, alone or in combination, can systematically inform – or eventually drive – the design and evaluation of new sound installations in public spaces.
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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 | 0.093 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".