Bibliographic record
Abstract
As a reader of this journal it is possible that you attach a certain significance to sound. Maybe you are a musician, an audio engineer, an architect, a foley artist, a marine biologist, or a composer of sonic art. Maybe you have studied sound in built environments, used sound in performance, in film or video, or researched sound underwater and among animals. You may have noticed how important sound can be in communicating mood, meaning and context. Perhaps when listening to a “soundscape”—sound heard in a real or “virtual” environment—you have been transported to another time, another place. Conversely, maybe you have experienced the-here- and-now even more acutely as a result of listening intently. Your awareness of sound—specifically your level of awareness of the acoustic environment at any given time—is an issue central to the interdiscipline of Acoustic Ecology (also known as ecoacoustics). The philosophy underpinning Acoustic Ecology is simple yet pro-found: its author—R. Murray Schafer, a musician, composer and former Professor of Communication Studies at Simon Fraser University (SFU) in Burnaby, BC, Canada—suggests that we try to hear the acoustic environment as a musical composition and further, that we own responsibility for its composition (Schafer 1977a, 205). Like many issues emerging from the explosion of ideologies in the late 1960s, the profundity of Schafer’s message is now hidden behind a single, soundbite-friendly issue: noise pollution. This is unfortunate since Schafer has far more to offer. However, some 22 years after his ideas were first fully articulated in print, they remain un-known to the general public and mostly unknown to environmental acousticians. Where Schafer is well known—within the contemporary music community—it is mostly for his large-scale, often site-specific, musical/theatrical work rather than his acoustic ecology. Composer John Cage was aware of both; when asked if he knew of any great music teachers, he replied “Murray Schafer of Canada” (Truax 1978, sleeve note).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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; both teacher heads agree on what is shown here.
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".