The areas of cybercrime prevention and improvement of legal regulation of human rights protection in cyberspace
Bibliographic record
Abstract
The article is devoted at identifying the main problems of human rights protection in cyberspace and developing modern and effective ways to improve legal regulation, increase the level of protection of users on the Internet and strengthen public confidence in the digital environment. The author analyzes and proposes ways to prevent cybercrime, one of the most serious problems of modern cyberspace, especially in the context of constant cyberattacks. Particular attention is paid to the analysis of current threats and problems related to human rights violations in cyberspace in the context of constant cyberattacks, which have significantly intensified after the full-scale invasion of russia. First of all, the authors analyzed the main national andinternationallegal acts regulating the protection of human rights in cyberspace. It is found that the existing legal framework governing Ukraine is somewhat irrelevant in the current environment, as it does not fully cover new threats arising from the development of information technologies. Motivated proposals were made to amend the current national legislation of Ukraine to improve the mechanisms for protecting human rights in cyberspace. The authors also raised the issue of personal data protection, in particular, analyzed the concept of the «right to be forgotten», which is particularly relevant in the context of the rapid dissemination of information on the Internet. The problems of protecting children’s rights in cyberspace, including such phenomena as cyberbullying, grooming and excessive exposure to violent content, are separately considered. The article emphasizes the importance of introducing special protection measures for children, in particular, by strengthening control over access to dangerous web resources, as well as intensifying efforts to raise awareness of parents and educators about threats in the digital space. The authors also analyzed measures of legal liability for cyberbullying in the United States and Canada. In addition, the article regulates in detail and substantiates the feasibility of developing a Strategy for Information Security of Children in Cyberspace and a Strategy for Digital Citizenship. The authors emphasize the need to introduce the institution of property liability for cyberbullying in Ukraine. The article concludes that a comprehensive approach to improving the legal regulation of cyberspace is needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".