Faddists, enthusiasts and Canadian divas: a model of the recorded music market *
Bibliographic record
Abstract
This paper constructs a model of the provision of commercial music in which some consumers (enthusiasts) enjoy diversity and others (faddists) prefer to follow what is popular. Record companies sign up bands, only some of whom will 'succeed' - a process modelled in a number of alternate ways - and radio stations broadcast recordings. Consumers hear music on the radio and purchase recordings, where the likelihood of purchase depends, in part, on the extent of radio airplay for a particular recording. We show that consumers' taste for diversity leads to under-entry in general and we illustrate the working of the model by considering the impact of a local content quota in broadcasting. It is shown that a quota that restricts the airtime devoted to foreign music induces a shift in the pattern of band entry into 'international' genres. But a mild quota is welfare-improving in this model: even though the diversity of local music is reduced, the quota increases the number of new entrants, drawn in by the increased profitability of success. We also discuss the consequences of a quota that requires increased broadcasting of 'new' music and show that, while the addition of the 'new' band component decreases the total amount of time devoted to listening to the radio by consumers (yielding a welfare loss), it does nothing to a record company's incentives to sign up new bands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".