Bibliographic record
Abstract
John Shepherd This intervention suggests that the recent and welcome emergence of fieldwork as a prominent feature of much current work in popular music studies has deflected attention from an undertaking that characterized the early days of popular music studies: that of developing from within the various protocols of cultural theory concepts to explain the meanings, significances, and affects that music as a socially and culturally constituted form of human expression holds for people. In tracing a shift from theoretical to ethnographic concerns in work carried out in popular music studies by musicologists, ethnomusicologists, social anthropologists, and sociologists, it is suggested that a renewed emphasis on theory in musicological work in popular music studies may be of consequence for the academic study of music as a whole. Beverley Diamond In response to the editor's question concerning theory and fieldwork, this colloquy argues that the two are inseparable. Further, the importance of fieldwork in providing "alternative theory" which challenges the consistencies of academic thinking is emphasized. For this reason, the article eschews disciplinary history as a means of tracing important theoretical currents in music scholarship and, instead, presents arguments which confront the hegemonies of any history, any discourse of intellectual continuity, positing incidents which expose the social contingencies of theory.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".