Legal regulation of personal data protection: GDPR and the legislation of the USA, Canada, and Ukraine
Bibliographic record
Abstract
The article provides a detailed analysis of the legal regulation of personal data protection in various jurisdictions, including the European Union, the United States, Canada, and Ukraine. Special attention is given to the General Data Protection Regulation (GDPR), which is one of the strictest international standards in this field. The main provisions of the GDPR are examined, such as the principles of lawfulness, fairness, transparency, purpose limitation, and data minimization, as well as the rights of data subjects, including the right to access, rectification, and erasure of data. The impact of GDPR on international businesses is analyzed, showing how it has forced companies worldwide to adapt their data processing systems to comply with European legal requirements. The section on the United States focuses on California state law, particularly the California Consumer Privacy Act (CCPA), which grants citizens rights over their personal data. Although the U.S. legislative framework is fragmented compared to GDPR, the CCPA is a significant step toward protecting the privacy of American citizens. Canadian legislation is represented by the Personal Information Protection and Electronic Documents Act (PIPEDA), which ensures the protection of personal data in commercial relationships. PIPEDA strikes a balance between business interests and citizens’ rights, providing flexibility in the use of personal data while adhering to principles of transparency and consent. The article also analyzes the process of harmonizing Ukraine’s legislation with the GDPR, which is a crucial step in the context of the country’s integration into the European legal space. Ukrainian legal reforms focus on strengthening citizens’ rights and improving mechanisms for controlling personal data processing. The article offers a comparative analysis of the discussed legal systems, highlighting key differences in data protection approaches. Unlike the EU, where regulation is comprehensive and stringent, U.S. laws are fragmented. In Canada, PIPEDA creates a more flexible system oriented toward the commercial sector. Ukraine, meanwhile, is on its way to full harmonization with European standards, which will enhance the legal protection of citizens in the digital economy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".