Bibliographic record
Abstract
Background The rich history of acoustic ecology is being critiqued and extended by a variety of work in sound studies and practice. Sonic geographers and ethnographers are researching the complex multispecies relationships that exist through sound around the world, as Max Ritts (2024) demonstrates with his work on the North West Coast of Canada. This in turn supports inventive multispecies kin making as Donna Haraway (2016) has called for. Composers and artists are developing and articulating methods and frameworks for such kin making. For example, Petri Kuljuntausta (2021) in his method of ‘acoustic nichephony’ for music performance, and Jonathan Gilmurray’s (2021) articulation of ‘ecological sonic art’. Contribution Whalefall is an audio piece I created with Miyuki Jokiranta. It listens to the stories of leviathans, mythic and real, connecting the social, political, and ecological challenges humankind now faces globally with the place of whales, in both our world and imagination. The piece includes archival material, interviews, field recordings, modular synthesis, and other sound design, using techniques such as ‘sonic bridges’, blurring whale song into the sound of modern synthesizers to create vital and material multispecies relationships for listeners. Through this it contributes methodologies and methods for sonic art to foster ecological engagement. Significance Whalefall was commissioned and premiered by Elizabeth Zimmerman for Austrian broadcaster ORF 1’s Kunst zum Hören program. Zimmerman is a highly respected curator and producer of audio art and the program is a key publishing platform in the field. The piece was aired and published in the program’s podcast in February 2025. I have further presented the work in ambisonic surround sound as part of the Design and Sonic Practice research group’s Listening Practice series at RMIT and have been invited by Professor Chris Speed to restage the work as part of Regenerative Futures public programs in 2026.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 0.040 |
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".