基于最新统计分类标准的数字经济发展水平测度

随着信息技术的发展,数字经济已经成为经济增长的"新引擎"。但由于缺乏权威的产业统计分类标准,学者们一直面临"数字经济研究缺乏数字依据"的尴尬境地。文章基于国家统计局公布并实施的《数字经济及其核心产业统计分类(2021)》中的分类标准,对各省份统计年鉴的数据进行重新整理,利用熵权法构建数字经济发展指数,测度了我国30个省份的数字经济发展水平,分析了各省份数字经济发展的差异以及时空特征。研究发现,2009—2019年我国数字经济产业发展迅猛,各项子产业都取得了长足的进步。相比较而言,数字要素驱动业发展速度略低于其他三个子产业;数字经济发展存在着明显的区域不平衡。东中部地区的数字经济发展状况明显优于西部地区,南方优于北方,而且区域不平衡有持续扩大趋势。     

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.     

民办幼儿园教师专业素养培训方式实验研究

随着现代教育的发展，提升幼儿园教师专业素养已成为促进学前教育发展的必然要求。以探究民办幼儿园教师专业素养为视角，提出通过干预行为提升教师专业素养水平的构想。实验随机选取成都市某民办幼儿园的20名教师为实验组，再选取与其办园环境、办园背景相似的另一所民办幼儿园的20名教师为对照组，对实验园教师的专业理念与师德、专业知识、专业能力3个维度进行干预。实验结果表明：与对照园相比，实验园教师的专业能力提升显著；与实验园实验前后相比，教师在专业知识、专业能力维度及专业素养总体水平方面均有提升。对教师培训的具体建议为：（1）单一与混合相结合的培训方式对提升民办幼儿园教师专业素养总体水平有现实意义；（2）民办幼儿园教师专业知识的提升需理论培训与自主学习相结合；（3）专家"现场诊断-指导式"培训方式对提升民办幼儿园教师专业能力有显著作用。综上，建议民办幼儿园在了解本园教师专业素养水平的基础上，针对教师专业素养的不同方面，分别采取不同的有效培训方式。     

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

     

Constructing Trustworthy Historical Narratives: Criteria,Principles and Techniques

Organizational scholars increasingly recognize the value of employing historical research. Yet the fields of history and organization studies struggle to reconcile. In this paper, the authors contend that a closer connection between these two fields is possible if organizational historians bring their role in the construction of historical narratives to the fore and open up their research decisions for discussion. They provide guidelines to support this endeavor, drawing on four criteria that are prevalent within interpretive organization studies for developing the trustworthiness of research: credibility; confirmability; dependability; and transferability. In contrast to the traditional use of trustworthiness criteria to evaluate the quality of research, the authors advance the criteria to encourage historians to generate more transparent narratives. Such transparency allows others to comprehend and comment on the construction of narratives, thereby building trust and understanding. Each criterion is converted into a set of guiding principles to enhance the trustworthiness of historical research, pairing each principle with a practical technique gleaned from a range of disciplines within the social sciences to provide practical guidance.     

Has Opposition to Immigration Increased in the United States after the Economic Crisis? An Experimental Approach

We employ two population‐level experiments to accurately measure opposition to immigration before and after the economic crisis of 2008. Our design explicitly addresses social desirability bias, which is the tendency to give responses that are seen favorably by others and can lead to substantial underreporting of opposition to immigration. We find that overt opposition to immigration, expressed as support for a closed border, increases slightly after the crisis. However, once we account for social desirability bias, no significant increase remains. We conclude that the observed increase in anti‐immigration sentiment in the post‐crisis United States is attributable to greater expression of opposition rather than any underlying change in attitudes.