The broken record: How the music industry is making sense of uncertainty and change
Bibliographic record
Abstract
Abstract As the recording music industry entered the digital era, significant turmoil emerged due to piracy and illegal downloading, and royalty payments to artists via streaming platforms. To date, the previous research examining the industry shift focused on consumer‐based decisions such as whether to download music illegally, and, more recently, experiences with streaming services. Using sensemaking as our lens, we examine changes and challenges through in‐depth interviews with music industry practitioners. The participants have been directly impacted by the digital shift in general and by specific issues such as piracy and poor compensation from streaming platforms. We develop a typology of industry members based on their development of sensemaking capabilities as they navigate the ever‐changing industry and the resulting influence on market practices.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.038 | 0.024 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".