Braindancing Through the Mainstream: Intelligent Dance Music as Popular Music
Bibliographic record
Abstract
Artists associated with the concept of intelligent dance music (IDM) maintain their underground identity while also benefitting from the commercial and celebrity elements inherent in mainstream popular music culture.In this thesis, I use the theory of subcultural capital to critically examine perceived notions of authenticity in IDM artists' identities and associated fandoms by exploring the ideological boundaries that separate the 'mainstream' from the 'underground'.To do so, I have adapted the theory of subcultural capital by replacing the term "conversion" with "translation" to more accurately describe how members of music subcultures work to maintain a fluid relationship with popular culture and keep their 'authentic' subcultural status.I critically examine the term IDM, a term that has been widely criticized by associated artists and online fandoms, but is still popularly used.Rather than using IDM as a genre term, which is common, I believe that IDM is better suited to describe a philosophy of music making and a way of knowing and being that emphasizes creative individuality and a critical interrogation of the passive consumption of cultural commodities.To explain this difference, I critically analyze the discourses surrounding Warp and Rephlex records in the early to mid-1990s, as well as more recent artist interviews and fan discussions.The main case studies of this paper include Aphex Twin, Squarepusher, and Autechre.Other key artists
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".