Bibliographic record
Abstract
Why would a punk band popular only in Indonesia cut songs in no other language than English? If you're rapping in Tanzania and Malawi, where hip hop has a growing audience, what do you rhyme in? Swahili? Chichewa? English? Some combination of these? Global Pop, Local Language examines how performers and audiences from a wide range of cultures deal with the issue of language choice and dialect in popular music. Related issues confront performers of Latin music in the U.S., drum and bass MCs in Toronto, and rappers, rockers, and traditional folk singers from England and Ireland to France, Germany, Belarus, Nepal, China, New Zealand, Hawaii, and beyond. For pop musicians, this issue brings up a number of complex questions. Which languages or dialects will best express my ideas? Which will get me a record contract or a bigger audience? What does it mean to sing or listen to music in a colonial language? A foreign language? A regional dialect? A native language? Examining popular music from a range of world cultures, the authors explore these questions and use them to address a number of broader issues, including the globalization of the music industry, the problem of authenticity in popular culture, the politics of identity, multiculturalism, and the emergence of English as a dominant world language. The chapters are written in a highly accessible style by scholars from a variety of fields, including ethnomusicology, popular music studies, anthropology, culture studies, literary studies, folklore, and linguistics. Harris M. Berger is associate professor of music at Texas A&M University. He is the author of Metal, Rock and Jazz: Perception and the Phenomenology of Musical Experience (1999). Michael Thomas Carroll is professor of English at New Mexico Highlands University. He is the author of Popular Modernity in America: Experience, Technology, Mythohistory (2000) and co-editor, with Eddie Tafoya, of Phenomenological Approaches to Popular Culture (2000).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".