Opera-to-opera adaptation revived: Barrie Kosky and Elena Kats-Chernin’s Monteverdi Trilogie at the Komische Oper Berlin, instrumentation, localization and community
Bibliographic record
Abstract
In 2012, Barrie Kosky opened his first season as head of the Komische Oper Berlin by staging adaptations of three Monteverdi operas. Alongside using more familiar forms of adaptation, Kosky commissioned Elena Kats-Chernin to adapt the scores, focusing on instrumentation. While opera-to-opera adaptation is comparatively rare today, it has a long and rich history. The article first proposes three categories of reasons for opera-to-opera adaptation in the past. It then sets Kosky and Kats-Chernin’s Monteverdi productions in the context of this largely forgotten history, arguing that a historical awareness allows the Monteverdi Trilogie to be understood in terms of continuity rather than rupture, and clarifies some of Kosky and Kats-Chernin’s approaches in terms of a vigorous but respectful engagement with opera and its place in the modern city, in terms of recuperating aspects of operatic reception now frequently lost, and in prompting a reconsideration of localization and community inclusion. The article argues that the combination of Kats-Chernin’s adaptation for a variety of western and non-western, classical and non-classical instruments, and the visibility of the instrumentalists, including migrant musicians, that Kosky’s staging enabled set the tone for Kosky’s tenure at the Komische Oper, especially in terms of community and inclusion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".