Bibliographic record
Abstract
In several compositions, Brian Cherney has reflected on the Holocaust and its impact, exploring how music can respond to such tragedy; his recent engagement with the poetry of Paul Celan is a natural extension of these preoccupations. This article offers a close reading of Cherney’s choral setting of Celan’s Tenebrae. The composer incorporates several additional texts that create a genealogy of the poem, from biblical passages to fragments of Dante and Hölderlin to accounts of the Holocaust itself; he arranges these texts to highlight semantic and sonic features of Celan’s work. Perhaps Cherney’s boldest move is his insertion of Hebrew letters, linking his composition to the long tradition of Lamentations settings—a link cemented by a quotation from Couperin’s Leçons de Ténèbres, which provides important motivic material. Through these additions, Cherney turns the poem towards us, inviting us to respond to its call for reflective witness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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 teacher head, 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".