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Bibliographic record
Abstract
The Routledge Companion to Sounding Art presents an overview of the issues, methods, and approaches crucial for the study of sound in artistic practice. Thirty-six essays cover a variety of interdisciplinary approaches to studying sounding art from the fields of musicology, cultural studies, sound design, auditory culture, art history, and philosophy. The companion website hosts sound examples and links to further resources. \n \nIn my chapter is entitled '> image > memory > sound > text'. I decided to approach this text about sound by starting with images and writing through the memories they trigger. Anspyaxw is a sound and photography installation which grew from my work with speakers of Gitxsanimaax, an endangered indigenous language in British Columbia, Canada. When I looked back through the photographs taken by Denise Hawrysio and myself during our fieldwork in and around the reserve of Kispiox, the first thing that struck me was the untold – the people, stories, sounds and relationships that got left behind in the process of turning fieldwork into artwork. Of course, fieldwork is itself a selective process from which much experience is, of necessity, either actively or passively filtered out, but perhaps the process of revisiting these images can help to unravel some of the apparently neatly tied threads of this project by working back through its heretofore undocumented history to the sounds that did – and didn’t – find their way into the final work.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.404 | 0.190 |
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".