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Record W2977371015 · doi:10.1111/1467-8322.12528

The unbearable weight of music: The intermezzo

2019· article· en· W2977371015 on OpenAlexaff
Steven M. Friedson

Bibliographic record

VenueAnthropology Today · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsTortureMusicalAestheticsRealmCommunitasLiminalityMode (computer interface)SociologyArtLiteratureHistoryLawHuman rightsPolitical science

Abstract

fetched live from OpenAlex

The affordances of musical experience, its capacity to become our mode of being‐in‐the‐world, especially in ritual situations, can be turned against us into an aversive sonic attack that bends the social arc into a liminality without end, a time in between that goes nowhere. And when this happens, we have entered the realm of music torture, a relatively recent innovation in that dark art that was ushered into the world in full force at the beginning of the 21st century. Music became part of a regime of no‐touch torture inflicted upon detainees in the ‘global war on terror’, itself a war without end. In this article, the author argues for an ontomusicology that understands music as ritual and ritual as music – in this case, ritual that inverts Victor Turner’s notion of communitas, with all of its attendant modes of being‐with, into a solitary mode of existence with no hope of escape, a musical ritual torture, a perpetual intermezzo.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.022
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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
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

Citations0
Published2019
Admission routes1
Has abstractyes

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