Female Subjectivities in the Words, Music, and Images of Progressive Metal
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".