The Acousmatic Question and the Will to Datafy: Otter.ai, Low-Resource Languages, and the Politics of Machine Listening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What happens when Nina Eidsheim’s acousmatic question—“Who is this?”—is delegated to machines? Machine listening processes turn sound and voices into data. This article explores the political stakes that accompany the automated extraction, processing, and analysis of human voices in machine listening, specifically speech recognition. While machine listening is promoted to users in the name of utility, inclusiveness, and access, it also serves corporate purposes: the expropriation and ownership of massive collections of data. This extractive will to datafy subtends commercial and state-based machine listening operations. We outline this problematic process though two case studies: the datafication of “low-resource” languages for speech recognition in India and the widespread adoption of Otter.ai transcription services in Canada and the United States during the COVID-19 pandemic. In both cases, noble aims—inclusion and access—are simultaneously coopted to serve corporations’ extractive projects, which are built on denying speakers the right to their own voices.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it