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Record W3161082060 · doi:10.1017/cbo9780511675850

Music, Sound and Space

2013· book· en· W3161082060 on OpenAlexaboutno aff
Georgina Born, Philip V. Bohlman, Gascia Ouzounian, Eric Clarke, Jonathan A C Sterne, Sumanth Gopinath, Nicola Dibben, Tom Rice, Andrew J. Eisenberg, Nicholas Cook, Byron Dueck, Tia DeNora, Suzanne G. Cusick, Richard Middleton

Bibliographic record

VenueCambridge University Press eBooks · 2013
Typebook
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)SoundscapeMusicPopular musicMusicologyMusicalCall and responseSubjectivityAestheticsMusic historySociologyVisual artsMusic educationArtAcousticsEpistemology

Abstract

fetched live from OpenAlex

Music, Sound and Space is the first collection to integrate research from musicology and sound studies on music and sound as they mediate everyday life. Music and sound exert an inescapable influence on the contemporary world, from the ubiquity of MP3 players to the controversial use of sound as an instrument of torture. In this book, leading scholars explore the spatialisation of music and sound, their capacity to engender modes of publicness and privacy, their constitution of subjectivity, and the politics of sound and space. Chapters discuss music and sound in relation to distinctive genres, technologies and settings, including sound installation art, popular music recordings, offices and hospitals, and music therapy. With international examples, from the Islamic soundscape of the Kenyan coast, to religious music in Europe, to First Nation musical sociability in Canada, this book offers a new global perspective on how music and sound and their spatialising capacities transform the nature of public and private experience.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.009
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0200.005

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 head0.030
GPT teacher head0.165
Teacher spread0.135 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations192
Published2013
Admission routes1
Has abstractyes

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