Eschewing Community: Black Metal
Bibliographic record
Abstract
ABSTRACT There is a great deal of literature that examines community orientations, in particular consumption‐based subcultures rooted in the appreciation of music scenes such as heavy metal and its subgenres. Much of this literature focuses on aspects of community maintenance, reaffirmation of shared identities and building of social bonds. In the present article, we report a study in which consumption of, and fandom in a specific scene in extreme metal, namely black metal, may lead to very unique consumer cultural orientations. Our analyses reveal that black metal fans' identities reside in a realm outside of a desired collective identification and tightly knit community, but rather one that uses signification , or representational means to convey meaning and belonging, as a way to signal repugnance with society and a reverence of individuality. The study engages a mixed qualitative approach utilizing interviews, observational research and content analysis to demonstrate how self‐identity related to the black metal music scene can thrive through an ideological and semiotic rejection of traditional community orientations seen in the majority of other extreme metal music scenes. This paper challenges traditional conceptualizations of group identity in music scenes by closely examining aspects of signification and fandom in black metal that represent a unique system of shared identities devoid of community building. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".