On the Drawbacks and Improvement of Legal System of Supervision on Securities Fraud in Cyberspace in China
Bibliographic record
Abstract
The law of internet securities fraud mainly concerns internet safety, securities offering, information disclosure and securities trading on internet, the imperfections of which mainly include the low level for legislation, short of fundamental legislation, imperfect special legislation, conflicts among traditional legislation, and short of rules of international coordination. The discreet measures for perfection should include promoting legislation level, establishing relevant provisions for cyberspace conduct and special law such as Electronic Securities Trading Law, and amending Securities Law to regulate the prominent securities fraud in cyberspace. At present, it is urgent for securities regulator to establish and perfect the relevant supervision rules. Key words: Cyberspace; Securities Exchange; Securities Fraud; Legal Supervision Resume: La loi de la fraude en valeurs mobilieres sur Internet concerne principalement la securite Internet, l’offres des titres, la divulgation de l'information et la transaction des titres sur internet, dont les imperfections incluent principalement un faible niveau de legislation, une absence de legislation fondamentale, une legislation speciale imparfaite, les conflits entre la legislation traditionnelle, et une absence de regles de coordination internationale. Les mesures discretes de la perfection devrait inclure la promotion du niveau de la legislation, l’etablissant des dispositions pertinentes pour la gestion de cyberespace et des lois speciales telles que la Loi sur le commerce electronique des titres, et la modification de la Loi des titres pour reglementer la fraude en valeurs mobilieres dans le cyberespace. A l'heure actuelle, il est urgent pour les regulateurs d'etablir et de perfectionner les regles de surveillance appropriees. Mots-Cles: cyberspace; taxes de transaction; fraudes en valeurs mobilieres; surveillane legale
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".