人才振兴背景下大学生志愿服务乡村缘由和影响因素分析

志愿服务乡村是社会参与人才振兴战略的重要方式,大学生在其中承担着重要角色。“乡愁文化”和“社会理性”是大学生志愿服务乡村缘由所在;性别、年级、学校类型、了解程度和志愿组织对大学生志愿服务乡村有显著影响。大学生志愿服务的着力点在于智力支持且具有文教活动倾向性,但也存在意愿与行动脱节、认知流于表面、能动性不强、缺乏思想引领以及对小微志愿组织的支持缺位等问题。乡愁文化嵌入大学文化、引入激励机制、打造志愿组织“大家庭”、牢守宣传育人“主阵地”、强化智力支持优势、补齐理论技能短板、关爱“小微组织”成长、构建“三位一体”支撑体系等可有效优化大学生志愿服务乡村。     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

论我国刑事诉讼中“非法证据”的界定标准

在刑事非法证据排除规则的具体运用中,"非法证据"的界定是当然前提。非法证据并非是任一证据合法性要件欠缺的广义非法证据,而专指取证主体通过严重非法的手段或者侵犯公民基本权利的方式所获取的证据材料。我国刑事非法证据包括非法言词证据和非法实物证据两类,两类非法证据在实际判断中虽存在差异,但都应围绕取证手段的非法性展开,分别考察取证手段是否违反法律规定以及违法的严重程度,以准确界定刑事非法证据的范围。     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

The context of employment discrimination: interpreting the findings of a field experiment

Although field experiments have documented the contemporary relevance of discrimination in employment, theories developed to explain the dynamics of differential treatment cannot account for differences across organizational and institutional contexts. In this article, I address this shortcoming by presenting the main empirical findings from a multi‐method research project, in which a field experiment of ethnic discrimination in the Norwegian labour market was complemented with forty‐two in‐depth interviews with employers who were observed in the first stage of the study. While the experimental data support earlier findings in documenting that ethnic discrimination indeed takes place, the qualitative material suggests that theorizing in the field experiment literature have been too concerned with individual and intra‐psychic explanations. Discriminatory outcomes in employment processes seems to be more dependent on contextual factors such as the number of applications received, whether requirements are specified, and the degree to which recruitment procedures are formalized. I argue that different contexts of employment provide different opportunity structures for discrimination, a finding with important theoretical and methodological implications.     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.