论毒品犯罪的死刑适用

我国毒品犯罪中被判处死刑的人数过多,造成这一现象的原因主要包括毒品犯罪案件在国内的大量存在、死刑核准权下放与刑事立法和刑事司法中的重刑主义。要有效地抑制毒品案件中的死刑人数,就应当树立轻刑、慎刑思想,采用适用法律时有利于被告的原则,充分发挥酌定情节的量刑作用,尽量以死缓制度代替死刑立即执行制度,尤其要严格控制运输毒品罪中的死刑人数。     

我国法治视野下刑罚目的的理性选择

刑罚目的是整个刑罚理论的基石,在刑罚目的理论中报应论与预防论的争论由来已久。由于我国法治的特殊背景和特殊预防自身理论的缺陷,注重特殊预防论在我国已没有存在的根基,相反却偏离了法治的方向。在基于刑罚目的二元论和综合衡量后,报应论前提下的一般预防是我们当前法治视野下的最佳选择。     

论行政处罚的“一事不再罚”原则

"一事不再罚"原则的规定是为了防止处罚机关滥用职权对相对人同一违法行为以同一事实理由处以多次行政处罚."一事"指符合一个行政违法构成要件的行为;"不再罚"指除非法律有特别规定,行政主体只能给予一次处罚.复杂的事数形态和法条竞合领域也应贯彻"一事不再罚"原则.     

单位普通累犯制度立法价值评判

刑法应当增设单位普通累犯制度。其构成要件之前后罪都应是故意犯罪 ,时间条件是后罪发生在单位直接负责的主管人员或其他直接责任人员因单位犯罪被判处有期徒刑以上刑罚的判决生效以后五年内 ,且前后罪中直接主管人员和其他直接责任人员均被判处有期徒刑以上刑罚。     

狱内行刑社会化的价值及其实现

行刑社会化的含义虽十分广泛,但狱内行刑社会化因较集中体现了自由刑与社会化的冲突而具有代表性.纯粹理性意义上的自由刑的本质应当仅仅限于将犯罪人限制在特定的活动空间里,但在实际执行时却必然产生种种负价值.行刑社会化作为执行自由刑宽和化的措施必然有利于消减这些负价值.累进处遇居于行刑社会化中心地位.我国狱内行刑社会化的横向分类重点在对监内罪犯的再次分类,建议以自然标准、犯罪标准和人格标准逐渐推进.纵向分级建议取消特严管理甚至一般严管并加大宽管的力度,其特宽管理可以考虑建立开放式监狱和半自由刑制度.操作上主要包括设立省级和狱内分类中心、将监狱分隔为普通管区、宽管区和特宽管区三部分以及实现罪犯的区际流动.     

国际体坛反兴奋剂的经济学分析

国际体坛反兴奋剂问题不仅是法律问题、政治问题、医学问题,也是一个经济学问题。本文通过建立一个简单的效用函数,分析了运动员使用兴奋剂的成本和收益因素,并根据这些因素,提出了通过提高运动员使用兴奋剂行为的预期成本来减少兴奋剂使用行为的一些措施。     

“The Motherhood Wage Gap for Women in the United States: The Importance of College and Fertility Delay”

One of the stylized facts from the past 30 years has been the declining rate of first births before age 30 for all women and the increase rate of first births after age 30 among women with four-year college degrees (Steven P. Martin, Demography, 37(4), 523–533, 2000). What are some of the factors behind womens decision to postpone their childbearing? We hypothesize that the wage difference often observed between like-educated mothers and non-mothers (Jane Waldfogel, Journal of Labor Economics, 16, 505–545, 1998a; Journal of Economic Perspectives 12(1) 137–156, 1998b) may be affected by the postponement of childbearing until after careers are fully established. Hence, we focus on college-educated women because they are typically more career-oriented than their non-college educated counterparts and also the group most often observed postponing maternity. We use individual-level data on women from the 1979 National Longitudinal Survey of Youth (NLSY79) in order to control for individual-level unobserved heterogeneity as well as human capital characteristics, such as actual work experience, in our empirical analysis. We estimate wage equations, first producing base-line results to compare to the existing literature. Then, we expand the basic wage equation model to address fundamental econometric issues and the education/fertility issue at hand. Our empirical findings are two-fold. First, we find that college-educated mothers do not experience a motherhood wage penalty at all. In fact, they enjoy a wage boost when compared to college-educated childless women. Second, fertility delay enhances this wage boost even further. Our results provide an explanation for the observed postponement of maternity for educated women. We argue that the wage boost experienced by college-educated mothers may be the result of their search for family–friendly work environments, which, in turn, yields job matches with more female-friendly firms offering greater opportunities for advancement.JEL Codes: J13 and J3     

论我国未成年人刑罚制度之完善——从原则及刑罚种类角度分析

我国现行有关未成年人的刑罚制度已不适应当前严峻的未成年人犯罪现实。本文从未成年人刑罚制度的指导思想、原则及具体刑罚规定着手,分析了其不足,并在此基础上结合现状提出了完善意见。     

报应主义与刑事政策

只有将刑罚目的奠基于报应主义为基础的功利主义之上,现代刑法才真正有可能不仅成为保护人民的大宪章,而且成为保护犯罪人的大宪章.报应主义一经脱胎于原始的报复便与其分道扬镳,它以意志自由为立论前提、道义责任为归责基础,主张罪刑均衡,彰显公正、人权、秩序之现代文明社会三大价值.虽然单纯的报应并不足以解释刑罚的目的,与预防相结合才是其真正归宿,但是报应优先、兼顾功利的一体化思想应当成为我们坚定的立场并贯穿于死刑、刑罚个别化和"严打"等诸多刑事政策的考量之中.     

Inference of Genetic Networks from Time Course Expression Data Using Functional Regression with Lasso Penalty

Statistical inference of genetic regulatory networks is essential for understanding temporal interactions of regulatory elements inside the cells. In this work, we propose to infer the parameters of the ordinary differential equations using the techniques from functional data analysis (FDA) by regarding the observed time course expression data as continuous-time curves. For networks with a large number of genes, we take advantage of the sparsity of the networks by penalizing the linear coefficients with a L ₁ norm. The ability of the algorithm to infer network structure is demonstrated using the cell-cycle time course data for Saccharomyces cerevisiae.