Higher Music Education and Employability in a Neoliberal World
Bibliographic record
Abstract
This open access edited collection shows how neoliberalism continues to shape higher music education institutions, curricula design and learning cultures, as well as the various ways of transitioning from education to work and the world of uncertainty and job insecurity currently being experienced by a younger generation of musicians.The book brings together empirical studies, activist voices, theoretical reflections and autoethnographic studies from a broad range of disciplines, work contexts and geographical regions. These contributions examine how race/ethnicity, gender and class pervade the creation, performance and teaching of music and create the context for the reproduction of social inequalities. They also illuminate the notions of employability, entrepreneurialism and meritocracy that underpin higher music education and the music labour markets in Italy, Portugal, the Netherlands, Sweden, Estonia, Hungary, Finland, the United Kingdom, the United States, Canada and China, and provide insights into the strategies used by musicians to manage their precarious working lives. Finally, this collection specifically highlights alternative pedagogical approaches and activist tactics for moving forward in the era of Black Lives Matter, #StopAsianHate and #MeToo. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by mdw - University of Music and Performing Arts Vienna.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".