监检程序中认罪认罚从宽制度的三维衔接
Three-Dimensional Connection of the Leniency System for Guilty Plea and Punishment Acceptance in Supervision and Prosecution Procedure
陈修勇1
作者信息
- 1. 华东政法大学刑事法学院,上海 200050
- 折叠
摘要
相较于《刑事诉讼法》第15条"权利导向"的制度设计,《监察法》第31条为职务犯罪适用认罪认罚从宽制度创设了更为严格的实体条件,即"主动认罪认罚+附加条件",呈现明显的"权力导向".适用条件的差异化导致实践中认罪认罚从宽制度适用的混乱,且《监察法》第31条在实践中并非法院裁判时的依据.为此,需从理念、制度和实践三个维度加强监检程序的衔接:从理念维度统一对制度价值的认识,发挥认罪认罚从宽制度对职务犯罪的特殊功能;从制度维度明确从宽处罚建议的"柔性"效力,防止监察调查结果对裁判起决定作用;从实践维度构建监察与司法的协调沟通机制,双向提升职务犯罪办案质效.
Abstract
Compared with the"rights-oriented"system design in Article 15 of the Criminal Procedure Law,Article 31 of the Supervision Law has established more stringent substan-tive conditions for the application of the leniency system for guilty plea and punishment ac-ceptance in duty crimes,namely"voluntary guilty plea and punishment acceptance plus addi-tional conditions",which shows an obvious"power-oriented"approach.The differentiation in application conditions has led to confusion in the application practice,and Article 31 of the Supervision Law is not the basis for court decisions in practice.Therefore,it is necessa-ry to strengthen the connection of supervision and inspection procedures from three dimen-sions of concept,system,and practice:From the concept dimension,we should unify the understanding of the value of the system and playing the special function of the system on duty crime;From the system dimension,we should clarify the"flexibility"effectiveness of lenient punishment suggestions to prevent the influence of supervisory investigation results on judicial decisions;From the practice dimension,we should construct a coordination and communication mechanism between supervision and justice to improve the quality and effi-ciency of handling duty crimes in both directions.
关键词
认罪认罚从宽制度/监察法/贿赂犯罪/监检衔接Key words
the leniency system for guilty plea and punishment acceptance/Supervision Law/bribery crime/the connection of supervision and prosecution引用本文复制引用
基金项目
2023年度上海市哲学社会科学规划一般课题(2023BFX011)
华东政法大学硕士研究生学术研究及社会调研项目(2022-3-038)
出版年
2024
NETL
NSTL
万方数据