预测性数据对刑事案件构成要素外延的补充  

Predictive Data Supplements the Extension of the Elements of Criminal Cases

作　　者：董杰[1] 姜人仁 Dong Jie;Jiang Renren(Criminal Investigation Police University of China,Shenyang Liaoning 110035)

机构地区：[1]中国刑事警察学院,辽宁沈阳110035

出　　处：《警学研究》2024年第4期46-58,共13页Police Science Research

摘　　要：传统的刑事案件构成要素理论包括五要素说、七要素说、纵向动态和横向静态要素说、信息化侦查要素说等,并且随着学者对侦查实践、犯罪活动规律和特点的思考,刑事案件构成要素理论体系已相对完善。然而,随着大数据思维和技术的发展与渗透,学界虽意识到应当将数据要素融入理论体系中,但并未意识到大数据背景下预测性数据的价值。而预测性数据作为由案件本身延伸出的有重要实践价值的信息,能够丰富刑事案件构成要素的外延,因此,可以遵循现有刑事案件构成要素理论构建逻辑,将预测性数据要素补充进原有要素理论。The traditional theory of the elements of criminal cases has five elements,seven elements,vertical dynamic and horizontal static elements,and information-based investigation elements.The traditional theory of the elements of criminal cases has five elements,seven elements,vertical dynamic and horizontal static elements,and information-based investigation elements.With the thinking of scholars on the investigation practice,the law and characteristics of criminal activities,the reconstruction system has been relatively perfect.Although the academic community has realized that data elements should be integrated into the theoretical system,it has not realized the value of predictive data in the context of big data.As an important practical value information extended from the case itself,the predictive data can supplement the extension of the elements of criminal cases.Therefore,it is reasonable to follow the existing theoretical construction logic of the elements of criminal cases and supplement the predictive data into the original element theory.

关 键 词：刑事案件构成要素 预测性数据 机器学习 

分 类 号：D924.11[政治法律—刑法学]