Legal Reflections: Optimizing Global Strategies Against Cyber Sexual Violence Through Comparative Perspectives
Bibliographic record
Abstract
In an era of deepening digitalization, cyber sexual violence (CSV) emerges as a global challenge, encompassing a broad spectrum of abuses including sexual harassment, exploitation, and extortion through digital platforms. The infamous Nth Room case in South Korea, where hundreds of women and minors were coerced into producing and sharing sexually exploitative materials online, starkly highlights the extreme and horrifying aspects of CSV. This incident not only galvanized global attention towards the menace of CSV but also prompted a reevaluation of the capability and efficiency of existing legal frameworks across various jurisdictions to combat such crimes.This study employs a comparative legal analysis to scrutinize how different legal systems, including those of the United States, Canada, European countries, and Asian countries like Japan, South Korea, and China, address the issue of CSV. By delving into some pertinent examples, the paper aims to uncover disparities in legal responses, the effectiveness of regulatory measures, and the limitations of these measures in preventing and punishing cyber sexual violence. Key findings underscore the urgent need for international legal standards and highlight the pivotal role of international cooperation and technological solutions in combating CSV. Ultimately, this research proposes a series of recommendations for legal reform, advocating for the development of a more effective legal framework that not only punishes perpetrators but also protects and supports victims, preventing the recurrence of such crimes.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".