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Record W4405506960 · doi:10.1017/s135577182400027x

The Haptic in Soundscape Composition

2024· article· en· W4405506960 on OpenAlexaff

Bibliographic record

VenueOrganised Sound · 2024
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsConcordia University
Fundersnot available
KeywordsHaptic technologySoundscapePerceptionPhenomenology (philosophy)Haptic perceptionComputer scienceComposition (language)AestheticsCognitive sciencePsychologyArtificial intelligenceAcousticsArtEpistemologyLinguisticsPhilosophyNeuroscience

Abstract

fetched live from OpenAlex

This article presents the concept of a ‘haptic aurality’ in soundscape composition, an aesthetic and perceptual model derived from visual art theory, media studies and phenomenology that extends the haptic beyond its common association to vibroacoustic phenomena in the sonic arts. Included in this framework are both the standard haptic arguments, from psychology and engineering, including notions of kinaesthesia and proprioception, and varied definitions of the haptic as a not necessarily tactile mode of knowing touch that involves synaesthesia, transmodal perception and philosophical notions of sensory dedifferentiation. In adapting this survey of sometimes contradictory accounts of the haptic as parameters for compositional analysis and application, the article simultaneously creates novel engagements between soundscape composition and acousmatic practice.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.010
Scholarly communication0.0040.006
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.339
Teacher spread0.310 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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