Mash‐Up Songs: Are There Any Exceptions to the Exclusive Rights in the Light of the Jordanian Copyright Protection and Related Rights Law?
Bibliographic record
Abstract
This article focuses on the phenomenon of mash‐up songs. The paper highlights the exclusive rights of authors and audio recordings producers under the Jordanian Copyright Protection and Related Rights Law (No. 22 of 1992) and its latest amendments in 2014. I will refer to UK, US, Australian and Canadian laws and case laws because there is no judicial precedent in Jordan relating to mash‐up songs, and Jordan as a developing country shall benefit from the experience of other jurisdictions, in this field. One of the exceptions and limitations to exclusive rights, that is, fair use defence was developed in England in the eighteenth century and the United States in the nineteenth century in Folsom v Marsh case. Moreover, the Canadian Copyright Act (R.S.C., 1985, c. C‐42) has recently added a “mash‐up exception”, therefore, it would be interesting to refer in brief, to the above exception. I will distinguish between the exclusive rights of authors and audio recordings producers that are applicable to mash‐ups. My aim in this paper is to determine whether there are any exceptions and limitations to these exclusive rights. This article concludes by recommending the inclusion of mash‐up exceptions to the Jordanian law.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".