Sharing Experiences Towards the Possibility of an Electroacoustic Ecology
Bibliographic record
Abstract
As I write this article, I am crossing Canada by train. Here I am even more aware than usual of my dependence on technology in order to do my work. I search through the cars for electrical outlets, and watch my battery level dwindling. Yesterday, while charging up the minidisk in the lounge car to do some more soundscape recording, I heard a group of urban twenty-somethings talking about the isolation they felt from their daily lives on this trip. They spoke of the comfort of a Walkman to avoid boredom and assert a connection to home through music, and wished that VIA Rail provided music inthe bar car. Then the conversation turned to the problem of musical choice, and how one person’s preferences might dominate the sound environment. As an acoustic ecologist, I am concerned about the way mainstream popular music blankets almost all acoustic environments. One of my joys of the last day has been scanning the radio dial, and hearing mostly snow or white noise, like the snow that surrounds the northern Ontario track we travel on. This is one place that is not dominated by an American top forty sensibility, and like Murray Schafer, I am glad of the predominance of snow in this environment.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".