From Pride to Prejudice to Shame
Bibliographic record
Abstract
This chapter presents an in-depth qualitative study of the inner workings of one niche extreme metal scene, namely black metal. Using data from the physical as well as virtual black metal scenes, the study explores how scene members manifest the tensions between their personal and communal identities, as well as how they negotiate the propagation of racism and xenophobia, both within and without online environments. The netnographic analyses presented draw on black metal scene members’ interactions in online forums and blogs showing sustained activity over an extended period of time, some spanning well over a decade-and-a-half. The authors also draw on data from observations at several concerts and festivals in North America and Europe, as well as personal, written reflections from an extreme metal music journalist who has struggled to find a balance between his appreciation of black metal music and some of the overt racism and violence propagated in the scene. Additionally, they present analyses from a series of interviews conducted with 12 black metal artists and fans from all parts of the globe. The authors cautiously contend that online interactions between members of niche music scenes such as black metal, wherein individual and collective identities are partially informed by xenophobia and influenced by socio-political structures, when extended to the larger populace through the publicly available Internet, can potentially serve as paradigmatic cases of how otherwise self-contained racist chatter could influence the larger public exposed to these transgressions to consider adopting racist ideologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".