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Record W4391792495 · doi:10.30535/mto.29.4.2

Female Subjectivities in the Words, Music, and Images of Progressive Metal

2023· article· en· W4391792495 on OpenAlexaff
Lori Burns

Bibliographic record

VenueMusic Theory Online · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArtLiteratureVisual arts

Abstract

fetched live from OpenAlex

Heavy metal scholarship affirms the genre to be dominated by male performers and points to a preponderance of patriarchal values and hypermasculinity, with performances contributing to an aesthetic production of misogyny, power, and intensity. The notion of heavy metal as a hegemonic discourse has been queried, however, by recent scholars who reveal metal to support a range of gendered and sexualized subjectivities. This paper examines how a specific metal vocalist—Tatiana Shmayluk (of the Ukrainian band Jinjer)—navigates the discourse of progressive metal to challenge hegemonic norms and create space for alternative female subjectivities. Jinjer’s defiance of genre boundaries and Shmayluk’s metal vocal expression emerge through a multi-faceted dialogue with an array of cultural references. To illuminate the unique blend of referentiality and creative expression within Jinjer’s work, this article offers analyses of three music videos: “I Speak Astronomy,” “Perennial,” and “Pit of Consciousness.” With the aim of understanding how Shmayluk navigates the discursive space of metal music, the selected songs are situated in relation to the subgenres to which they refer, and specifically to male-fronted metal bands that mobilize similar thematic materials. The close readings of these music videos are grounded in the existing analytic literature on metal music, with consideration of genre-based compositional, stylistic, and expressive elements to unveil Shmayluk’s challenges to the constraints upon “femininity” in metal music.

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)0.001
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 score0.386
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.244
Teacher spread0.203 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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