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Record W2091663084 · doi:10.1093/jrma/125.1.93

Musical Decay: Luciano Berio's <i>Rendering</i> and John Cage's <i>Europera 5</i>

2000· article· en· W2091663084 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

VenueJournal of the Royal Musical Association · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Studies and Postmodernism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArtOperaMusicalVisual artsJohn CageSymphonyEmptinessRendering (computer graphics)Performance artArt historyComputer sciencePhilosophyComputer graphics (images)

Abstract

fetched live from OpenAlex

Restoration and reproduction have served as two of the primary means by which the present has approached the past. These practices are the focus of Luciano Berio's Rendering and John Cage's Europera 5 , two recent works that draw upon earlier compositions. In Rendering , Berio ‘restores ’ the drafts for what would have been Schubert's Tenth Symphony. Contrary to conventional restorations, Berio not only builds up the sketch materials but also fragments them, having Schubert's themes disappear into musical voids. Europera 5 looks back at eighteenth- and nineteenth-century opera, which is presented in a collage of live performance and reproductions. During the course of the work, opera gradually disappears into a world of reproductions, losing its vocality and presence. In both compositions, restoration and reproduction ultimately make the past more distant and inaccessible. A similar use of these two practices occurs in recent visual artworks by Igor Kopystiansky and Mike and Doug Starn. Both the musical and visual artworks create scenes of decay, in which the past appears as crumbling and the present as an emptiness.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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