Community at the extremes: The death metal underground as being-in-common
Bibliographic record
Abstract
Abstract This article asks what the early death metal underground teaches us about the relations between community and aesthetics. After tracing the emergence of death metal as a genre, the article examines the accounts of musicians, artists and recording engineers collected in Jason Netherton’s Extremity Retained (2014). Drawing on contemporary theories of non-human agency, research in animal studies, and Continental philosophies of community, the article focuses on ‘brutality’ as a crucial marker of death metal’s political significance, arguing that this involved experiments with new ways of embodiment that outstrip humanist presuppositions about what a body can do. Then, the article examines how international tape trading networks allowed for the emergence of forms of ‘being-in-common’ that cannot be understood in merely human terms. Finally, the article argues that the death metal underground’s particular importance lies in its linking of more-than-human practices of community with a focus on death and negativity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".