非主流主流化:大学生兼职的社会学分析

社会发展、学校改革、家庭贫困、个人原因等使大学生兼职由非主流化走向主流化,甚至庸俗化。大学生兼职的动机由锻炼自己变为养活自己,在社会贬低大学生与大学生自我贬低的双重打压下,大学生兼职主流化、庸俗化不可避免,这要求社会、学校与家庭认真对待大学生兼职主流化、庸俗化问题,积极引导大学生兼职,防止因为兼职所带来的负面影响制约大学生成长与进步。     

论民事补偿制度

民事补偿是由非违法者对受害人的损失予以填补的民事法律制度,非违法者对受害人损失的补偿是特定情形下感激的道德义务的法律化。民事补偿制度有行为人补偿制度、受益人补偿制度和共同体补偿制度等三个类型。但无论是行为人、受益人还是共同体予以补偿,都建立在适法事实的基础上,因而民事补偿的性质是民事义务。民事补偿不具有惩罚性,其范围原则应限于受害人的直接损失。     

创新、升级与培育——2013上海文化消费报告

在全国文化消费方兴未艾的背景下,上海文化消费尤为繁盛。在载体方面,创新与多元化趋势增强。由政府主导的基础型公共文化服务体系进一步完善,消费产品更加多样化,但仍存在公共文化机构数量不足、消费理念薄弱等问题。电影节、书展、动漫节、艺术节、艺博会五大节庆类公共文化平台在消费产品多元化、亲民化、国际化方面的趋势更加明显。新媒体作为市场主导的自主型文化消费载体,发展迅猛。网购、APP、微博经济、网络文学消费稳步增长。在客体方面,假日旅游和出境游消费火爆,私人影院等体验式消费备受青睐,演艺、奢侈品消费呈爆发式增长。在主体方面,上海已形成高、中、低3类消费主体并存的文化消费格局,阶层化倾向日趋明显。     

彝族孝文化文献《赛特阿育》的文本源研究

在贵州彝区的毕摩文献里,有一部反映孝文化的文本《赛特阿育》,与流传于汉区的董永行孝相关文献有密切关系。从情节内容看,《赛特阿育》是对董永行孝相关文本的彝文翻译改写,将其与董永变文、宝卷中的相关文本加以比较研究发现,《天仙配宝卷》应是《赛特阿育》的文本源。     

异质性产品质量与出口加成率

相对于已有研究分析生产率、目的市场选择和市场竞争等因素对出口加成率影响的视角,本文采用2000-2007年企业层面数据测算了28个细分制造业出口产品质量指数及按照会计方法处理的出口加成率,对中国制造业行业产品质量与出口价格加成率的关系进行实证分析.研究结果表明:出口加成率总体并没有随产品质量而提升,但影响因产品质量的异质性而存在差异,劳动密集型制造业出口产品质量显著提高了加成率,而技术和资本密集型产业仍处于阶段性“加成率陷阱”.     

利用非齐次指数函数构造GM(1,1)模型背景值的新方法

文章以灰色GM(1,1)模型背景值的优化为研究目的,讨论了传统背景值构造方法适用于低增长指数序列、对高增长指数序列拟合产生滞后误差的原因,提出了利用非齐次指数函数x(1)(t):A.eBt+C在区间[k-1,k]上与横坐标轴所围实际面积来构造背景值的新方法,并对其合理性进行了证明;在不同发展系数a条件下,比较了背景值构造方法对模拟和预测精度的影响,给出了新背景值条件下GM(1,1)模型的适用范围;大量的数据模拟和模型比较表明,新背景值构造方法提高了背景值的精确性及灰预测模型的拟合精度和预测精度.     

不完全数据多重插补的Bootstrap方差估计

当对插补所得的“完整数据集”使用标准的完全数据统计方法的时候,往往会低估插补估计量的方差.Bootstrap方法(自助法)是非参数统计中的一种重要的统计方法,是基于原始观测数据进行重复抽样,能充分的利用已知数据,不需要对未知总体进行任何的分布假设或增加新的样本信息,进而再利用现有的统计模型对总体的分布特性进行统计推断.本文首先运用多重插补的方法对缺失数据进行了插补,之后利用Bootstrap方法对插补之后的数据进行了插补统计量的方差估计,结果表明运用Bootstrap方法进行插补统计量的方差估计更科学更准确.     

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Children represent a large underserved population of “therapeutic orphans,” as an estimated 80% of children are treated off‐label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or “borrowing”) of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure‐response information for antiepileptic drugs to pediatrics.     

Control‐based imputation for sensitivity analyses in informative censoring for recurrent event data

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.     

Inequality in Social Insurance Participation and Generosity: Do Firm Characteristics Matter?

Income inequality in China has been much documented and debated. However, one important aspect of income inequality – social insurance inequality – has been largely understudied. We use a large national panel of firm data to examine the possible influence of various firm characteristics on the inequalities in social insurance participation and generosity in the period 2004–07. Our findings reveal substantial ownership sector and regional gaps in social insurance provision by firms, with the private domestic sector and those in the central region lagging behind. Unionized firms, larger firms, and firms with greater market shares also tend to provide better social insurance to their employees. While being an exporting firm is linked with greater social insurance participation and generosity, being a purely exporting firm, however, is associated with reduced social insurance generosity. Lastly, highly educated workers receive substantially better social insurance coverage than their less educated peers. These social insurance inequalities are highly relevant as China pushes forward with establishing a unified social insurance system across urban‐rural boundaries, ownership sectors and industries.