Bibliographic record
Abstract
The advent of digital technology has brought unprecedented convenience but also significant challenges to personal privacy, particularly in China. The phenomenon of “human flesh search” (HFS), facilitated by both legal internet platforms and illicit information trade, has become a source of major privacy violations. This paper explores the effectiveness of China's current legal measures, particularly the Criminal Law as Article 253(A), in safeguarding personal information against these threats. It also examines the implications of these legal shortcomings on individuals’ daily lives and privacy. The dangers of human searches and their impact on the reality level are analyzed through detailed profiling of the parties involved in cases related to cyber violence. This report analyzes these legal inadequacies, comparing them with international standards, and suggests enhancements to better safeguard personal privacy in the digital age. By learning from international practices and rigorously adapting and enforcing its laws, China can better safeguard its citizens against privacy violations and set a standard for personal information protection in the digital era.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".