The Three-Legged Stool of Value of Copyrighted Music: Hertzian Radio, SiriusXM, and Spotify (The Working Paper Version – v2)
Bibliographic record
Abstract
Pricing copyrighted works or assets has always been a difficult task given the information good character of such works. Doing it in the digital era is even more challenging. This paper proposes an approach to infer the competitive market value of copyrights in music from choices made by users namely the operators of Hertzian radio (HR), satellite radio (SiriusXM), and interactive music streaming services (Spotify). The inferred competitive values, which are obtained independently, fall in the same ballpark, although they need not be equal or even close as business models and cost structures differ significantly between those music delivery technologies. Nevertheless the estimated competitive market values of music copyrights clearly indicate that rightsholders are significantly shortchanged and poorly served by the current copyright pricing framework. Appendix A presents the data from which one can infer the value of music in HR. Appendix B presents an overview of the debate before the Copyright Board of Canada following the presentation of the model from which the value of music in HR can be inferred. Read the first version of this publication La tarification des œuvres ou actifs protégées par le droit d'auteur a toujours été une tâche difficile étant donné le caractère ‘biens d’information’ de ces œuvres. Le faire à l'ère du numérique est encore plus difficile. Ce cahier propose d’inférer la valeur de marché à partir du comportement et des choix des utilisateurs, principalement les opérateurs de radio Hertzienne, de radio par satellite (SiriusXM) et de services de musique en ligne (Spotify). Les valeurs ainsi inférées séparément sont de niveaux comparables bien qu’il ne soit pas nécessaire qu’il en soit ainsi étant donné les différences importantes entre leurs modèles d’affaire et leurs structures de coûts. Les valeurs estimées montrent clairement que les ayants-droits sont significativement sous-compensés et donc mal servis par le système actuel de tarification des droits d’auteur. Deux appendices sur les données et le modèle utilisés pour inférer la valeur de la musique dans la radio commerciale au Canada complètent le cahier. Consulter la première version de cette publication
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".