Musical Decay: Luciano Berio's <i>Rendering</i> and John Cage's <i>Europera 5</i>
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".