Innovation and Diversity Redux: Analyzing Musical Form and Content in the American Recording Industry, 1990–2009
Bibliographic record
Abstract
Using the American recording industry as a case study, this article analyzes innovation and diversity concurrently and outlines the analytical purchase gained from doing so; examines the effects of performer incumbency and combinatorial role patterns, thereby offering an empirical application of the “role as resource” perspective (Baker and Faulkner ); and provides data on an underexplored era in which the emergence of digital technology has had wide‐ranging repercussions. Regressing measures of innovation (form) and diversity (content) on incumbency status and combinatorial role patterns reveals that innovation and diversity operate through distinct collaborative patterns. New artists are found to be carriers of musical innovation, and while performing artists with autonomy over the roles of songwriter and producer are more likely to be progenitors of musical diversity, innovation emerges from role specialization. Artistic roles and performer attributes, moreover, come together in particular ways to influence diversity and innovation depending on the environmental context. Post compact disc (CD) format era, innovation wrought by producer specialization is predominant, but the music is devoid of diversity. I conclude by arguing that the manner in which configurations of diversity and innovation interact has implications both for cultural production and reception.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".