Empirical evidence on musicians? Careers and earnings: the role of copyright
Bibliographic record
Abstract
The available data on authors’ and artists’ earnings come from three different sources: (a) government statistics (census, labour market surveys, tax); (b) questionnaire surveys of specific professional groups; and (c) collecting society payments. For the purposes of assessing the possible contribution of copyright law to authors’ and artists’ earnings, two aspects are of particular interest. (1) The level and distribution of earnings for cultural workers, compared to other professions; (2) Earnings from the principal artistic activity compared to other sources of earnings. The evidence shows that the median (typical) earnings of authors and artists are well below national average wages, although a small number of authors and artists earn very well. These winner-take-all characteristics of cultural markets are even more pronounced in the music sector where the top 10% of composers/songwriters account for almost 90% of the total earnings of the profession. Most professional authors and artists rely on a second job to survive. For composers, earnings from copyright royalties account on average for less than a quarter of creative income; for musicians, earnings from royalties account for about 1% of creative income. Copyright law in its current form is a weak and skewed regulatory mechanism for awarding authors and artists.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".