Bibliographic record
Abstract
"Well, listen. .. "is a sound composition about the acoustic community of Toronto Island and Toronto Harbour. The project explores how people create and experience acoustic community, how perceptions of the soundscape are related to attitudes about nature and culture, and how power relationships are articulated through sound. The project is based in environmental cultural studies and in sound ecology, notably the work of Williams (1973), Schafer (1977), Westerkamp (2002) and Truax (1984), and concludes seven months of soundwalks, interviews, composition, editing and field research. Participants discussed the soundscape of Toronto Island, noise pollution in Toronto Harbour and the relationship between sound, community and ecology. These interviews were edited and re-assembled in a manner inspired by the contrapuntal voice compositions of Glenn Gould. Field recordings reflect the complex mix of natural, social, and industrial sounds that make up the soundscape of the harbour, and document the acts of sound walking and deep listening that are the core methods of soundscape research. The composition creates an imaginary aural space that integrates the voices and reflections of the Island's acoustic community with the contested soundscape of their island home. The project paper outlines the theory and methods that informed the sound composition, and further explores the political economy of noise pollution, especially in relation to the Docks nightclub dispute and to current research in sound ecology.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".