Bibliographic record
Abstract
Guest Editor Patricia Shehan Campbell introduces this Special Issue of the Journal of Popular Music Education focusing on ‘Girls and Women in Popular Music Education’. Campbell briefly sets the scene, articulating something of the urgency of the need to pay more and long overdue attention to women in popular music – both in and for popular music education. Campbell introduces articles by each of the authors featured in this Special Issue, outlining the thrust and key points in each case. She introduces Claire Anderson’s article on spectaculars; Lloyd McArton’s paper on Toronto indie musicians; Clayton Dahm’s writing on Malian musician Oumou Sangaré; Kelsey Guo and Mari Shiobara’s piece about the enduring appeal of Teresa Teng; J. Michael Kohfeld’s article about LGBTQ+ representation in college music curricula; Carol Shansky’s article on well-known harmonica players; Ailbhe Kenny’s study of African migrant DJs in Ireland; and Juliana Cantarelli Vita’s paper about the Brazilian ensemble, Dita Curva.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".