Bibliographic record
Abstract
Abstract. This article reviews the scholarly literature on music produced from the 1980s to the present by Western far-right political actors. Research on this topic has been conducted primarily by sociologists and political scientists rather than musicologists, the article claims, and these scholars often focus on the political, economic, and social consequences of the music, rather than its sound or performance. The article argues that the terminology in this literature is inconsistent, with some scholars using the term “White power music” to refer specifically to a style that originated from the British skinhead scene of the 1980s and other scholars employing it to refer to far-right music in general. Despite many attempts to address the phenomenon of far-right music holistically, the literature has overemphasized skinhead music while largely ignoring other genres, including those of more recent and politically successful far-right movements. The article argues that developing more detailed, rather than generalized, analyses and giving more attention to music beyond skinhead genres would serve the needs of scholars and anti-far-right activists alike, providing the detailed accounting necessary to map, analyze, or counteract far-right politics. How to cite this article: Teitelbaum, Benjamin R. 2021. “The Study of Far-Right Music”Music Research Annual 2: 1–29. About the author: Benjamin R. Teitelbaum is Associate Professor of Musicology and International Affairs at the University of Colorado Boulder. His research focuses on ideology and expressive culture in contemporary radical nationalist, populist, and neofascist movements. He is the author of two books, Lions of the North: Sounds of the New Nordic Radical Nationalism (2017) and War for Eternity: The Return of Traditionalism and the Rise of the Populist Right (2020). His research has received awards from the Institute for the Study of Radical Movements, the Society for Ethnomusicology, and the International Studies Association. His latest book was named a Financial Times “Editor’s Pick,” listed among the 2020 “Books of the Year” by CapX and Estadão, and became an international best seller in 2021. Teitelbaum’s writings have appeared in major European and American media outlets, including the New York Times, the Wall Street Journal, The Nation, The New Statesman, and The Atlantic. ISSN 2563-7290
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".