A Stroke of Genius or Copyright Infringement? Mashups and Copyright in Canada
Bibliographic record
Abstract
Mashups, songs created by combining pieces of two or more pre-existing sound recordings into one new sound recording, allow anyone with access to a computer and sound editing software to engage with and participate in the (re)creation of culture. Among other purposes, mashups allow individuals to critique artists, to make satirical statements on the nature of pop music or the music industry, to make new works out of existing cultural expression, and to craft homages to favourite artists or works. This article examines the extent to which mashups are permitted by copyright law in Canada. It is structured as follows. First, it will provide an introduction to mashups, defining the term and discussing the popular emergence of mashups. Second, it will examine whether mashups prima facie infringe copyright in Canada. Third, it will look at whether mashups are protected by the fair dealing defence. This article will demonstrate that many mashups created and disseminated in Canada prima facie infringe copyright. Furthermore, a large number of mashups that prima facie infringe copyright will not be protected by the fair dealing defence as it is currently being applied by Canadian courts. The question, therefore, of the extent to which the Canadian Copyright Act should be revised to permit individuals to create and disseminate mashups without infringing copyright merits discussion during Canada’s ongoing process of copyright reform. One possibility for reform is to incorporate a right to create and disseminate transformative works within the Canadian Copyright Act.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".