Ethnomusicology and the Indian Ocean: On the Politics of Area Studies
Bibliographic record
Abstract
Abstract This article draws on the recent boom in Indian Ocean studies to build a framework for registering the Indian Ocean in ethnomusicology. We show how the human experiences of movement across the Indian Ocean expanse have conditioned the musical traditions of ports and islands, and we put ethnomusicological writings on places like Zanzibar and Oman into dialogue with those from Mauritius and Singapore. We address how ethnomusicology’s area studies paradigm has inhibited musical studies of the Indian Ocean Region (IOR); the specter of comparative musicology; and the perils of modern Indian Ocean populations in light of postcolonial ethnonationalisms. Untuk membangun pengertian Lingkup Samudra India (Indian Ocean Studies) dalam bidang ethnomusikologi, artikel ini bersumber dari kumpulan studi sarjana-sarjana ilmu sosial yang merintis penelitian ini. Pengalaman migrasi manusia, dari pulau ke pulau dan pelabuhan ke pelabuhan, telah membentuk kebudayaan bermusik yang sudah lama dalam lingkup yang begitu luas. Untuk menggambarkan keberadaan ini, kami menghubungkan penelitian melalui dialog dari Zanibar dan Oman, Mauritius sampai Singapura. Artikel ini menyimpulkan: bagaimana paradigma “area studies” menjadi rintangan untuk mempelajari musik dari Lingkup Samudra India; perbedaan pandangan antara “comparative musicology” dari jaman penjajahan dengan penelitian ini; dan bahaya kesukuan dan nationalisme bagai penduduk Lingkup Samudra India sekarang ini.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.078 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".