‘Our Sonic Playground’: A model for active engagement in urban soundscapes
Bibliographic record
Abstract
Abstract ‘Our Sonic Playground’ is the name of a public event organized by the author in 2013 for the Museum of Contemporary Art (MCA) in Chicago. This project attracted the participation of a number of local artists interested in sound, music and the environment. Many were members of the ‘World Listening Project’ and Midwest Society for Acoustic Ecology. ‘Our Sonic Playground’ suggested that this event could serve as a model for actively engaging the public in soundscape awareness, an oftenneglected aspect of life in urban and other environments. This model is potentially useful for future engagements by providing a ‘recipe’ or set of practical suggestions for educators and ‘critical citizens’ as it relates to broader concerns with environmental change, urbanism, and awareness of place and public space. The author’s pedagogy of play and free improvisation emphasizes the importance of community and a type of aural-tactile engagement with listening and sound making that critically employs the physical, social and aesthetic role of media technology. This interest in public engagement is informed by the foundational work in the early 1970s, by the ‘World Soundscape Project’, and subsequent activities led by Canadian composers R. Murray Schafer, Hildegard Westerkamp and Barry Truax. Partnerships with local arts institutions, community organizations, led by faculty and students at The School of the Art Institute of Chicago and the city’s large creative community show how art and technology can reach out of the academy and into daily lives of people by effecting the acoustic identities of cities in positive and socially meaningful ways.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".