Obligations Concerning Digital Rights Management (DRM) in International Law - What are the Strategies for Developing Countries to Deal with DRM from Legal Perspective?
Bibliographic record
Abstract
With the rapid development of Digital Rights Management (DRM), Technological Protection Measures (TPM) and Rights Management Information (RMI) as components of DRM have become essential tools for safeguarding copyright. The WIPO Copyright Treaty (WCT) and the WIPO Performances and Phonograms Treaty (WPPT) were the first international agreements to establish protections for TPM and RMI. Subsequently, modern Free Trade Agreements (FTAs) such as the United States-Mexico-Canada Agreement (USMCA), the Regional Comprehensive Economic Partnership (RCEP), and the EU-Vietnam Free Trade Agreement (EVFTA) have introduced more complex and comprehensive protection requirements for TPM and RMI. This study reviews the core obligations in these treaties and analyzes the domestic implementation in Vietnam and Mexico—two representative developing countries that have recently implemented copyright reforms. Additionally, this study explores the potential impacts that may arise in the application of DRM. Based on the above findings, this study provides policy recommendations for developing countries, including seeking flexibility in FTAs, improving domestic legal frameworks, seeking technical assistance, and paying attention to the social impacts that reforms may bring.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".