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Record W2117773238 · doi:10.1162/lmj_a_00192

Dataffect: Numerical Epistemology and the Art of Data Sonification

2014· article· en· W2117773238 on OpenAlex
Mitchell Akiyama

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

VenueLeonardo Music Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsMcGill University
Fundersnot available
KeywordsSonificationMetaphorRepresentation (politics)AbstractionAestheticsEmpathyEquivalence (formal languages)BureaucracyEpistemologySound artSociologyArtVisual artsCognitive scienceComputer scienceArt historyHuman–computer interactionPsychologyPhilosophyLinguisticsPerformance artSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This article examines the history of sonification in sound art, focusing on the role that data play in influencing artistic creation and aesthetic experience. The author discusses sonified data artworks that go beyond the simple representation of information and that offer critiques of what Horkheimer and Adorno described as the dehumanizing notion of equivalence at the heart of the bureaucratic, capitalist economy. Concluding with a discussion of his installation Seismology as Metaphor for Empathy (2012), the author suggests that representing data through sound can engender powerful affective responses to the cold abstraction of information.

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)0.005
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.337
Teacher spread0.266 · 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