Sound sellers: musicians' strategies for marketing to industry gatekeepers
Bibliographic record
Abstract
Purpose The relationship between musicians and the music industry has often been depicted as a dichotomy between creativity and commerce with musicians conflicted between their roles as artists and their roles as marketers of sound. Recently, marketing researchers have problematized this dichotomy and suggested musicians perceive these roles as inevitable and indivisible. However, the processes of how musicians market their sound to the industry gatekeepers remain unclear. This study seeks to find the key industry gatekeepers for musicians and how musicians sell their personal sound to them. Design/methodology/approach Using an interpretative phenomenological approach, ten interviews with professional musicians across different music genres provided insight into the strategies musicians use to market their sound to industry gatekeepers. Findings In total, three key gatekeepers and the five strategies that musicians use to sell their sound are identified. The gatekeepers are record labels, other musicians and consumers. Musicians sell their sound to these gatekeepers through the externally directed strategies of using social media to build relationships, defining their personal sound through genre and creating a unique sound, and through the internally directed strategies of keeping motivated through sound evolution and counting on luck. Research limitations/implications The findings are limited by the small number of musicians interviewed and the heterogeneous representation of music genres. Originality/value The study contributes to theoretical understandings of how musicians as cultural producers market their sound in a commercial industry.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".