Listening Beyond the Human: The Autonomous Recording Unit and the Ethics of Sound in Biodiversity Conservation
Bibliographic record
Abstract
Sound technologies and sound sensor networks play a crucial role in our understanding of biodiversity loss in conservation biology and the environmental sciences. Among these technologies, the autonomous recording unit (ARU) has been widely used for studying longitudinal biodiversity loss. This article draws on fieldwork conducted in 2016 and 2017 at a bioacoustics research laboratory to explore the significance of the ARU, developed by Wildlife Acoustics, as the central component of the research network for biodiversity conservation. While it is commonly acknowledged in Science and Technology Studies (STS) that research instruments are not neutral data collectors, this article examines how the ARU is deployed and programmed, and how it transcends the limitations of human-centered listening by (a) shifting the focus away from the perceiving human subject and (b) promoting a global ethic of response and responsibility as sound becomes more democratized in scientific practices.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".