Making Sweet Music Together: The Affordances of Networked Media for Building Performance Capital by YouTube Musicians
Bibliographic record
Abstract
Following Miller, who looked at offline performance capital for musicians and discovered important gender and genre impacts, we examined the role of gender and genre in the development of performance capital for YouTube top cover song artists. This case study suggests that online performance capital on YouTube is slightly different than offline performance capital, and benefits from the affordances of networked media, and specifically YouTube. While there is some gender-based homophily in channel linking behaviors, there are also connections between weakly tied individuals with respect to video category, meaning that musicians are linking to others outside of the music community and vice versa. While music video channels tend to link to other music video channels, and non-music channels tend to link to other non-music channels, the most popular videos tend to post from multiple categories including both music and non-music. Findings suggest that being a long-time poster and having a rich and diverse network are likely elements of building performance capital for YouTube musicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".