Bibliographic record
Abstract
1. EXPLORING THE CONNECTION The term soundscape composition did not exist when I started composing with environmental sounds in the mid-1970s. Through a variety of fortunate circumstances and because of what the 1970s were in Vancouver and Canada - artistically inspiring and moneys were available for adventurous and culturally, socially, politically progressive projects - I had discovered that environmental sounds were the perfect compositional ‘language’ for me. I had learnt much while working with the World Soundscape Project at Simon Fraser University, about listening, about the properties of sound, about noise, the issues we face regarding the quality of the sound environment and much more. This in combination with learning to record and to work with analog technology in the sonic studio allowed me to speak with sound in a way I found irresistible. In addition, the start-up of Vancouver Co-operative Radio gave us the - at that time rare - opportunity to broadcast our work. It was a place where cultural exploration and political activism could meet. It was from within this exciting context of ecological concern for the soundscape and the availability of an alternate media outlet that my compositional work - now often called soundscape composition - emerged. And it came as a surprise to me, as I had never thought of composing nor of broadcasting as a professional choice in my life.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".