MétaCan
Menu
Back to cohort
Record W3007532869 · doi:10.1080/17458927.2020.1715113

Introducing audio-vision into evidence: the impact of audio recordings and their technical limitations in police use of force cases

2020· article· en· W3007532869 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Senses and Society · 2020
Typearticle
Languageen
FieldComputer Science
TopicDigital Media Forensic Detection
Canadian institutionsCarleton University
Fundersnot available
KeywordsAudio visualNeglectOfficerSound recording and reproductionComputer sciencePsychologyMultimediaAcousticsLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.291
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it