Digital sampling and culture jamming in a remix world: what does the law allow?
Bibliographic record
Abstract
This article looks at the way in which intellectual property law in particular copyright and trademark law deals with the "free culture" practices of digital sampling and culture jamming. It considers the recent US case on digital sampling, Bridgeport Music Inc v Dimension Films Inc, and its relevance to Australian law, along with the critical issues of ‘substantial part’, moral rights and fair dealing. This analysis is applied to a short case study of MP3 Blogs. In relation to culture jamming the article considers the legality of using trademarks as part of social commentary under Australian, Canadian and US trademark law. The article explores the way in which Creative Commons licences and the current "Fair Use Review" by the Commonwealth Attorney General can solve some of the existing problems and enhance participation in our ever growing remix culture. The article concludes by calling for greater clarity in the law in relation to the "free culture" practices of sampling and culture jamming in order to sponsor social and creative innovation.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.076 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".