Editorial Introduction: Popular Music in Education, Special Issue
Bibliographic record
Abstract
This Popular Music in Education (PME) special issue includes contributions discussing developments in several countries, including Argentina, Australia, Brazil, Canada, Germany, Singapore and the United States. It covers a range of approaches, exploring technology, hermeneutics, theory, guitars, jazz, songwriting, DIY/DIWO, politics and music industry perspectives. As music institutions have increasingly opened their doors to popular music, this has inevitably led to a greater level of interest in how you teach and learn popular music. PME is presenting a louder presence within Popular Music Studies (PMS), as the ground prepared by PMS has made space for a wave of new PME courses and students to sweep through educational contexts. In the wake of such expansion, this special issue intends to promote a further understanding of pedagogical best practice. The development of PME is something that is long overdue, and that seems likely to greatly expand and enrich the frame of PMS.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".