Music of Contingency: A Musical Topic of Cosmic Horror in Depictions of “The Music of Erich Zann”
Bibliographic record
Abstract
H.P. Lovecraft was a twentieth-century American writer whose short story “The Music of Erich Zann” has inspired musical works in several genres. This story was written within Lovecraft’s aesthetic of cosmic horror, an aesthetic which portrays the disintegration of a subject following exposure to the unknown terrors of reality. While cosmic horror shares some characteristics with Immanuel Kant’s and Edmund Burke’s sublime, it differs in that it denies the objective distance and foundation of reason required by Kant and Burke to allow the subject to gain pleasure from the experience. Considering two musical responses to “The Music of Erich Zann” by composers Raymond Wilding-White and Alexey Voytenko, this paper finds a similar distinction between musical expressions of the sublime and cosmic horror. Both compositions use some techniques from the musical topics of ombra and tempesta that scholar Clive McClelland describes as musical emanations of the sublime; however, both also present techniques beyond these topics that deny the listener a foundation in familiarity and any single musical frame. As a result, this paper argues that these compositions exemplify a musical topic of cosmic horror that is similar to but distinct from the topics of musical sublimity. This ‘music of contingency,’ titled in reference to philosopher Quentin Meillassoux’s work, emerged to express the new anxieties and pluralities of the twentieth century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".