Bibliographic record
Abstract
"Contributions by Nilanjana Bhattacharjya, Benjamin Burkhart, Ivy Chevers, Martha I. Chew Sánchez, Athena Elafros, William García-Medina, Sara Goek, Eyvind Kang, Junko Oba, Juan David Rubio Restrepo, and Gareth Dylan Smith In Scattered Musics, editors Martha I. Chew Sánchez and David Henderson, along with a range of authors from a variety of scholarly backgrounds, consider the musics that diaspora and migrant populations are inspired to create, how musics and musicians travel, and how they change in transit. Its authors cover a lot of ground: cumbia in Mexico, música sertaneja in Japan, hip-hop in Canada, Irish music in the US and the UK, reggae and dancehall in Germany, and more. Diasporic groups transform the musical expressions of their home countries as well as those in their host communities. The studies collected here show how these transformations are ways of grappling with ever-changing patterns of movement. Different diasporas hold their homelands in different regards. Some communities try to recreate home away from home in musical performances, while others use music to critique and redefine their senses of home. Through music, people seek to reconstruct and refine collective memory and a collective sense of place. The essays in this volume-by sociologists, historians, ethnomusicologists, and others-explore these questions in ways that are theoretically sophisticated yet readable making evident the complexities of musical and social phenomena in diaspora and migrant populations. As the opening paragraph of the introduction to the volume observes, "What remains when people have been scattered apart is a strong urge to gather together, to collect." At few times in our lives has that ever been more apparent than right now"--
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.019 |
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".