Copyright And The Economic Effects Of Parody: An Empirical Study Of Music Videos On The Youtube Platform, And An Assessment Of Regulatory Options
Bibliographic record
Abstract
This is the third in a sequence of three reports on Parody & Pastiche, commissioned to evaluate policy options in the implementation of the Hargreaves Review of Intellectual Property & Growth (2011). Study I presents new empirical data about music video parodies on the online platform YouTube; Study II offers a comparative legal review of the law of parody in seven jurisdictions; Study III provides a summary of the findings of Studies I & II, and analyses their relevance for copyright policy. Links to all three studies are available from http://www.ipo.gov.uk/pro-ipresearch/ipresearch-year/ipresearch-year-2013.htm Study III is structured as follows: First, we discuss the empirical findings from Study I. A sample of 8,299 user-generated music video parodies was constructed relating to the top-100 charting music singles in the UK for the year 2011. The key findings are: Parody is a signifi cant consumer activity: On average, there are 24 user-generated parodies available for each original video of a charting single. There is no evidence for economic damage to rights holders through substitution: The presence of parody content is correlated with, and predicts larger audiences for original music videos. The potential for reputational harm in the observed sample is limited: Only 1.5% of all parodies sampled took a directly negative stance, discouraging viewers from commercially supporting the original. Observed creative contributions were considerable: In 78% of all cases, the parodist appeared on camera (also diminishing the possibility of confusion). There exists a small but growing market for skilled user-generated parody: Parodists who exhibit higher production values in their works attract larger audiences, which can be monetized via revenue share with YouTube. Secondly, we present a distilled discussion of the legal treatment of parodies in seven jurisdictions that have implemented a copyright exception for parody (Australia, Canada, France, Germany, Netherlands, UK, and USA). The underlying principles (including economic and constitutional) governing divergent legal approaches are identified, and a list of policy options is presented. Thirdly, we provide a synthesis of the legal analysis and the empirical data. Each of the policy options identified in Study II is examined for its likely impact on the empirical sample gathered in Study I. Finally, some recommendations are made.
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 | 0.008 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".