Mapping Music Education Research in the UK
Bibliographic record
Abstract
Over the past 25 years there has been an increasing and worldwide research interest in music education, embracing a range of disciplines and perspectives. As well as particular research foci on the nature of curricula, musical behaviour and development, new research literatures have been developed that link music education with ethnomusicology, psychology (including neuropsychobiology, cognitive and developmental psychology), history, sociology and philosophy, as well as with mainstream studies in pedagogy. This review creates a ‘map’ of these various and related literatures in order to (i) provide a summative overview of the current breadth and depth of available research knowledge for actual and potential users, and (ii) create a research development agenda that embraces indicators of possible research priorities for the immediate future. The review is also a celebration of the major UK impact on these research literatures. An introduction (Swanwick) leads into a series of linked overviews, focusing first on research concerned with individual musical development (Hallam and Lamont), then on the potential impact to musical learning of social group membership (O’Neill and Green) and schooling (Cox and Hennessy) and concluding with an ethnomusicological perspective (Farrell) and coda (Welch).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.042 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".