Bibliographic record
Abstract
Building on the increasing popularity of applied ethnomusicology approaches since the early 1990s, a “second wave” of developments in the field’s methodology and practice raises various questions about its topics, terms and definitions, as well as the worksites and motivating factors for such applied work. Why has applied ethnomusicology come to focus on what Timothy Rice (2013) calls “music in times of trouble”? This article argues that the term applied ethnomusicology has taken on new definitions and meanings since about 2007. Yet what are the recently popularized definitions of applied ethnomusicology, and why has the field been redefined? The worksites of applied projects have long involved institutions. In the second wave, though, the scope of involved private, public and third sector institutions is broadening. What are the diverse types of institutions in which applied ethnomusicology work occurs today? As well, what are some factors that currently motivate the development of applied work in music? I explore aspects such as concrete problems in society, the repurposing of universities and academic trends and histories. I reflect on challenges proposed by the second wave.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".