Key terms in popular music and culture
Bibliographic record
Abstract
Acknowledgments. Notes on contributors. Introduction: Putting It Into Words: Key Terms for Studying Popular Music Bruce Horner (Drake University) and Thomas Swiss (Drake University). Part I: Locating Popular Music in Culture:. 1. Ideology: Lucy Green (University of London). 2. Discourse: Bruce Horner (Drake University). 3. Histories: Gilbert Rodman (University of South Florida). 4. Institutions: David Sanjek (BMI Archives). 5. Politics: Robin Balliger (Stanford University). 6. Race: Russell Potter (Rhode Island College). 7. Gender: Holly Kruse (La Salle University). 8. Youth: Deena Weinstein (DePaul University). Part II: Locating Culture in Popular Music. 9. Popular: Anahid Kassabian (Fordham University). 10. Music: David Brackett (SUNY Binghamton). 11. Form: Richard Middleton (University of Newcastle upon Tyne). 12. Text: John Shepherd (Carleton University). 13. Images: Cynthia Fuchs (George Mason University). 14. Performance: David Shumway (Carnegie Mellon University). 15. Authorship: Will Straw (McGill University). 16. Technology: Paul Theberge (Concordia University). 17. Business: Mark Fenster (Yale Law School) and Thomas Swiss (Drake University). 18. Scenes: Sara Cohen (University of Liverpool). Index.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.039 |
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".