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Record W2910729000 · doi:10.5206/notabene.v11i1.6617

Cover Songs and Tradition: A Case Study of Symphonic Metal

2018· article· en· W2910729000 on OpenAlexvenueno aff
Benjamin Hillier

Bibliographic record

VenueNota bene · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSymphonyMusicalCanonLiteraturePopular musicClassical musicKey (lock)Cover (algebra)ArtPhenomenonAestheticsConstruct (python library)Visual artsHistoryPhilosophyEpistemologyComputer science

Abstract

fetched live from OpenAlex

This paper examines the role of cover songs in the continuation of tradition, and in the formation of a musical canon. It explores the connections between ‘classical’ and heavy metal music as expressed by musicians of said genres, specifically those who partake in both. Furthermore, I argue that the practice of covering works from the Western art music canon in the metal genre, evinces the consequent development of the symphonic metal sub-genre. An embedded investigation attests to Western art music having inspired numerous metal musicians, who have in turn covered said music as a means to show their respect for the tradition. As such, cover versions are essential to continue one tradition in a new direction. Ultimately, these cover versions of classical works liaise classical music and heavy metal, resulting in the formation of the symphonic metal tradition. Covering music also strengthens a musicians’ position as authentic artists by demonstrating their belonging to two rites, and through their work of synthesizing grounds for the fusion of aforementioned rites. This research provides a further basis for examining the same phenomenon in other genres of music that demonstrate inter- and intra-generic links. It also provides a base for research into how rock and metal bands construct their own notions of tradition, canon, and authenticity through the music that they create and adapt.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.141
GPT teacher head0.247
Teacher spread0.107 · 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.

Study designQualitative
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

Citations1
Published2018
Admission routes1
Has abstractyes

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