Music and Digital Media: A planetary anthology
Bibliographic record
Abstract
Anthropology has neglected the study of music. Music and Digital Media shows how and why this should be redressed. It does so by enabling music to expand the horizons of digital anthropology, demonstrating how the field can build interdisciplinary links to music and sound studies, digital/media studies, and science and technology studies. Music and Digital Media is the first comparative ethnographic study of the impact of digital media on music worldwide. It offers a radical and lucid new theoretical framework for understanding digital media through music, showing that music is today where the promises and problems of the digital assume clamouring audibility. The book contains ten chapters, eight of which present comprehensive original ethnographies; they are bookended by an authoritative introduction and a comparative postlude. Five chapters address popular, folk, art and crossover musics in the global South and North, including Kenya, Argentina, India, Canada and the UK. Three chapters bring the digital experimentally to the fore, presenting pioneering ethnographies of an extra-legal peer-to-peer site and the streaming platform Spotify, a series of prominent internet-mediated music genres, and the first ethnography of a global software package, the interactive music platform Max. The book is unique in bringing ethnographic research on popular, folk, art and crossover musics from the global North and South into a comparative framework on a large scale, and creates an innovative new paradigm for comparative anthropology. It shows how music enlarges anthropology while demanding to be understood with reference to classic themes of anthropological theory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".