Bibliographic record
Abstract
Explorations and analysis of soundscapes have, since Canadian R. Murray Schafer's work during the early 1970's, developed into various established research - and artistic disciplines. The interest in sonic environments is today present within a broad range of contemporary art projects and in architectural design. Aesthetics, psychoacoustics, perception, and cognition are all present in this expanding field embracing such categories as soundscape composition, sound art, sonic art, sound design, sound studies and auditory culture. Of greatest significance to the overall field is the investigation of sound, site and the social, and how the spatial, the visual, and the bodily interact in sonic environments, how they are constructed and how they are entangled in other practices. With the Seismograf special issue Fluid Sounds, we bring this knowledge into the dissemination of audio research itself by introducing a new format: The Audio Paper. The purpose of the audio paper is to extend the written academic text: to present discussions and explorations of a certain argument or problem in sound. The audio paper is an extension of expressive means: Not only words and syntax are means of expression – so are tempo, time, voice, sound and music. Our goal is to bring analytical and performative awareness to academic means of expression, and the audio paper provides us with a new and experimental platform to do so. Our thoughts about and definition of the audio paper is explained in the first text of the issue: Audio Papers – a manifesto.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".