Protection of Personal Non-Property Rights in the Field of Information Communications: A Comparative Approach
Bibliographic record
Abstract
The issues which arise in connection with the use of information technologies are analysed in the paper. The attention is focused on the protection of non-property rights, such as honour, dignity, business reputation, violated on the Internet. It is noted that today there is a significant increase in the volume of legal regulation in this area. Nevertheless, there are still significant gaps in the protection of human rights, violated on the Internet. The current level of development of these processes objectively requires the creation of effective mechanisms and means to protect human rights and freedoms, including personal non-property rights. The existing ways of protection of persons on the Internet, such as judicial protection and self-defense are compared in the paper, there advantages and drawbacks are revealed. The types of violations of non-property rights of persons on the Internet are investigated. The specific attention is paid to cyberbullying. Some issues typical for communication on the Internet, such as difficulties in identifying where exactly the offence was committed and which court the claim should be addressed as well as the identification of the offender are revealed. Some shortcomings in the legal regulation of protection of persons on the Internet and ways to eliminate them are analyzed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".