Bibliographic record
Abstract
Noise, an underground music made through an amalgam of feedback, distortion, and electronic effects, first emerged as a genre in the 1980s, circulating on cassette tapes traded between fans in Japan, Europe, and North America. With its cultivated obscurity, ear-shattering sound, and over-the-top performances, Noise has captured the imagination of a small but passionate transnational audience.\n\nFor its scattered listeners, Noise always seems to be new and to come from somewhere else: in North America, it was called "Japanoise." But does Noise really belong to Japan? Is it even music at all? And why has Noise become such a compelling metaphor for the complexities of globalization and participatory media at the turn of the millennium?\n\nIn Japanoise, David Novak draws on more than a decade of research in Japan and the United States to trace the "cultural feedback" that generates and sustains Noise. He provides a rich ethnographic account of live performances, the circulation of recordings, and the lives and creative practices of musicians and listeners. He explores the technologies of Noise and the productive distortions of its networks. Capturing the textures of feedback—its sonic and cultural layers and vibrations—Novak describes musical circulation through sound and listening, recording and performance, international exchange, and the social interpretations of media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".