Bibliographic record
Abstract
Contemporary music analysis podcasts are engaged in an ongoing project of deconstructing pop songs and recoding them as valuable cultural objects. In this article, I understand podcasting as an extension of the popular music press and trace the affective strategies hosts use to elevate and evaluate pop songs new and old. I argue that music podcasts have seen a slow but steady departure from the conventions of critical distance and affectless, disembodied engagement to adopt an embodied, emotive response to the music that moves them. Drawing on theories of feminist affect, fan studies, and reactivity, I read the incorporation of fannish affect and the celebration of male creators’ emotive response to pop music as a continuation of a middlebrow sensibility that informs the popular music press writ large. Popular music analysis podcasts, on their surface, are a project of taking pop music seriously. When we scratch this surface, however, what we find is a mixed bag of tactics that seek to affirm the majority white, majority male creators as uniquely positioned to analyze, evaluate, and respond to music, much of which they are encountering considerably after it has already achieved the success that codes these songs as “popular” in the first place.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".