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Record W4283118296 · doi:10.5206/notabene.v15i1.15030

Music of Contingency: A Musical Topic of Cosmic Horror in Depictions of “The Music of Erich Zann”

2022· article· en· W4283118296 on OpenAlexaffvenue
Graeme Dyck

Bibliographic record

VenueNota bene · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSublimeMusicalSubject (documents)PleasureArtLiteratureMusical analysisFoundation (evidence)AestheticsArt historyHistoryPsychologyComputer science

Abstract

fetched live from OpenAlex

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.

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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