The Adoption of the American Fair Use in Gulf States – A Comparative Analysis of Author’s Exceptions in Common Law and Civil Law Countries
Bibliographic record
Abstract
The fair use doctrine is one of the most important and enigmatic doctrines in the copyright law of the US. Copyright scholars and experts have suggested introducing flexible open-ended copyright exception such as fair use instead of the restrictive exceptions currently adopted in developing countries to accommodate copyright laws to suit social and cultural needs, and keep pace with digital technological developments. The article studies the merits and demerits of this proposal from the perspective of Gulf States that follow the continental European system of author’s rights. It reviews limitations \nand exceptions in the copyright laws of the Gulf States and compares them to the US, UK, Australia, Canada, France and Germany. The article argues that reforming exceptions in the copyright laws of Gulf States is critically needed to suit the ambitious agenda of these countries to become knowledge-based economies that foster innovation and creativity. It examines the various options available to legislators in Gulf States to reform their copyright regimes, and suggests the adoption of liberal system of exceptions to suit citizens’ digital needs in the 21st century. Finally, the article provides recommendations and suggestions for reforming copyright laws and limitations and exceptions in Gulf States, and concludes with a summary \nof its main conclusion.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".