A NEW CYBERBULLYING LAW? EXTENSION OF LEGAL INTERPRETATIONS IN CHINA AND RUSSIA
Bibliographic record
Abstract
Cyberbullying is a form of psychological violence that is intentional, repeated, characterized by power imbalance, and uses cyberspace as its medium. Cyberbullying can be much more vicious than the ‘traditional’ face-to-face bullying because it is not limited by time and space, difficult to detect, and the aggressors often enjoy anonymity and impunity. Moreover, cyberbullying can exist as a self-contained phenomenon in cyberspace, which means that the aggressor and the victim may not know each other in the real world. Bearing these facts in mind, we need to answer two important questions: 1) Is cyberbullying a new type of offense? 2) Do we need a new anti-cyberbullying law? Scholars around the world are divided on these issues. While some countries, like the United States and New Zealand, have directly criminalized cyberbullying, others, like Australia and Canada, are simply amending their existing laws or extending their interpretations. This paper examines the legal situation in China and Russia, the two countries which do not have any specific laws regarding cyberbullying. The research is built upon the analysis of applicable laws and judicial decisions. The case studies overview the situations when victims of cyberbullying sought legal protection in court. The paper concludes that neither China nor Russia needs to pass a new anti-cyberbullying law. They are already doing adequate work to amend and interpret the existing civil, administrative, and criminal laws in order to counter cyber-offenses. However, more effort needs to be done to remove procedural barriers to litigation and prosecution, such as the costly and cumbersome notarization process in Russia, or the private character of the prosecution of defamation in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".