Bibliographic record
Abstract
This article presents an examination of the World Ear Project, a radio program developed by Charles Amirkhanian and Richard Friedman in August 1970 in Berkeley, California, that reframes the birth of acoustic ecology, conventionally viewed as a discipline created by R. Murray Schafer in Canada during the 1960s and ’70s. The article begins by presenting the sociocultural and musical circumstances that led to the creation of this radio program, emphasizing the compositional work of Luc Ferrari, whose interest in recording ambient environments was a major influence on Amirkhanian and Friedman. Analysis of archival broadcasts suggests that the World Ear Project was a space in which composers could contribute and use the medium of field recording to interrogate their creative practices, while average KPFA listeners also steadily provided diverse soundscape recordings of environments from across the globe. Further analysis of broadcasts, scripts, and other archival materials related to the World Ear Project reveal that this egalitarian, listener-driven radio series exemplified the democratic, activist principles at the core of its parent organization, the Pacifica Foundation. Crucially, we find many soundscape recordings related to contemporaneous Cold War inflection points and decolonization efforts on the World Ear Project, highlighting the broader progressive contours in which the program was conceived. The article concludes by suggesting that the World Ear Project reframes our understanding of the early history of acoustic ecology, having developed independently of Schafer’s work in Canada and taking an optimistic view of recording technology and the soundscapes of the modern, industrial world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".