Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.000 |
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 it