Rethinking metal aesthetics: complexity, authenticity, and audience in Meshuggah's «I» and «Catch Thirtythr33»
Bibliographic record
Abstract
The unusual complexity of two recent recordings by the extreme metal band Meshuggah has resulted in a strongly divided reception amongst fans, providing the opportunity to reconsider some common conceptions of metal aesthetics and to contribute to subtler ways of understanding taste and social demographics. Spanning twenty-one and forty-seven minutes respectively, I (2004) and Catch Thirtythr33 (2005) surprised fans with their unusual lengths (both recordings considered by the band to be single songs), complex song writing, and, with Catch Thirtythr33, the band's use of programmed drums. In response to interviewers' questions about each of these factors, the members of Meshuggah have made remarks that have been widely accepted among fans and rock journalists but that also seem to contradict their compositional practices and sometimes even their own previous statements. In my thesis, I investigate this discrepancy and its implications for how the concepts of authenticity and aesthetic values vary in metal discourses using concepts derived from critical theory, music theoretical analysis, and sociology. By uncovering several diverse aesthetic values through these discourses, I argue for an alternative to traditional class-based models of metal fans, one that will acknowledge the wide variety of aesthetic values found amongst metal audiences in this study.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".