Construction Rules of the Responsibility Determination System for Crimes of Precious and Endangered Wildlife in Intelligent Trials
The pilot and application of intelligent trial systems is a major attempt to promote the modernization of China's trial work.It should not only play the role of quickly searching for legal norms,but also generate a responsibility determina-tion system through case sorting,theoretical review,and other methods,providing reference suggestions for the judgment of key issues in cases.The trial of the crime of precious and endangered wildlife is often faced with the problem of whether to judge and how to judge whether the suspect clearly knows that the wildlife involved in the case are precious and endan-gered wildlife under national key protection.The intelligent trial system should start from the practical needs of cases of en-dangering precious and endangered wildlife,clarify the legal logic of the key issues of the case,namely the"knowing"prob-lem,and set up a responsibility determination system in the intelligent trial system to further optimize the trial effect.The ba-sic principles for constructing a liability determination system for the crime of endangering precious and endangered wildlife in intelligent trials should be based on legislation,cases as connotations,and theories as supplements,forming a library of legal norms for similar cases,and compiling a liability determination system based on the constituent elements of the crime.Establish a list of similar wildlife cases,generate key warning and analysis for trials.Form a library of academic viewpoints on similar cases and generate substantive trial reference suggestions for identifying"knowingly".Set important seasonal re-minder points to fully utilize promotional effectiveness in assisting in the generation of legal documents.Building a responsi-bility determination system in an intelligent trial system helps to better achieve the goal of intelligent trial systems assisting practical trials.
Intelligent trialWildlifeCriminal compositionKnowinglyConfirmation of responsibil-ity
万方数据
维普
