Introducing audio-vision into evidence: the impact of audio recordings and their technical limitations in police use of force cases
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
This article explores the technical limitations of audio recordings and how those limitations impact the reliability of sound evidence in police use of force cases. In audiovisual recordings, audio is often assumed neutral, redundant or to have the same limitations as its visual counterpart. Bringing together film theorist Michel Chion’s concept of audio-vision and the technical specifications of mobile audio recording, this article highlights how design priorities and compression processes can influence the way sound evidence is perceived. By failing to acknowledge audio recordings as distinct from their visual counterparts, they are rendered invisible and are therefore under scrutinized throughout legal processes. This neglect becomes notably problematic in cases of police use of force where audio/visual recordings often work to bolster the already privileged officer testimony.
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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 | 0.001 |
| 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 it