Laws and Regulations Governing Copyright Protection in the Digital Space (Comparative Study in Global Documents, U.S. Law, Canadian Law, and Iranian Law)
Bibliographic record
Abstract
Today, information technology tools and the digital space have impacted all human societies, and everyone is utilizing this space in some way. However, this space also brings new challenges, one of the most serious being the protection of content creators' rights. While the protection of creators' rights in the physical space has been a focus for about two centuries, with various regulations and laws enacted, this issue is still in its infancy in the digital space and faces numerous difficulties. Nevertheless, countries have sought to protect copyright in this new environment, both independently and through the adoption of regional and global treaties. The main objective of this article is to provide a comparative look at the measures taken in global documents, as well as in the legal frameworks of the United States, Canada, and Iran regarding this issue. To achieve this goal, three international legal documents and the legal frameworks of the United States, Canada, and Iran were analyzed using content analysis. Key legal documents were first identified, and their provisions related to the protection of creators were determined. The text of the laws was studied and interpreted in light of the characteristics of the digital space. The findings indicate that attention to the characteristics of the digital space in global documents and those of the United States and Canada is greater than in national laws and regulations. However, some legal provisions in Iranian documents, such as the Law on the Protection of Authors, Composers, and Artists, the Law on the Translation and Reproduction of Books and Publications and Audio Works, the Law on the Protection of Computer Software Creators, the Electronic Commerce Law, the Law on the Punishment of Individuals Engaging in Unauthorized Audiovisual Activities, the Press Law, regulations and rules for computer information networks, the draft comprehensive law on literary and artistic property rights and related rights, and the draft law on data protection and privacy in the digital space, can also be applied to the digital environment. In conclusion, given the characteristics of the digital space and its ever-increasing development, Iranian lawmakers should pay special attention to these aspects in future legislation.
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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 | 0.010 | 0.015 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".