高校思想政治教育主体研究综述

思想政治教育主体是高校思想政治教育实践活动的重要组成部分,历来是思想政治领域中的热点问题.迄今为止,学术界已对高校思想政治教育主体的内涵、特征、类型、地位、作用等基本理论问题进行了广泛而又深入的研究.本文较系统地梳理了学者们的理论研究成果,以将对该问题的研究进一步引向深入.     

后新时期国内新闻舆论监督研究述评

从舆论监督的难点、隐性采访与新闻舆论监督、新闻舆论监督与反腐败、新闻舆论监督与新闻官司 ,以及加强新闻舆论监督的途径等五个方面 ,对后新时期新闻舆论监督研究的现状进行描述 ;从操作设计与理论探讨、内部分析与外部研究、取向多元与学科建构三个维面 ,对后新时期国内新闻舆论监督研究中的存在问题进行检讨 ;对新世纪的新闻舆论监督研究方向提出建设性思考     

中国现代文学期刊研究的综合考察

中国现代文学期刊是现代中国社会与文学变革的催生剂,也是作家作品生产的重要载体。总结中国现代文学期刊的研究经验,特别是总结近三十年来的研究经验,将会促进中国现代文学研究学术质量的整体性提升。而进一步提高期刊研究的理论自觉,拓展研究的广度与深度,则是中国现代文学期刊研究所面临的迫切任务。     

黄山市音乐考级的现状调查与研究

音乐考级是对音乐能力进行的分级考试。黄山市作为皖南山城,具有浓厚的徽州文化底蕴,在当地老百姓的观念中学习艺术是旁门左道,自从十年前第一个音乐考级点建立起,至今已有三个考级单位,参与培训并获得考级证书的学员近万人。极大程度上推动了黄山市素质教育的发展,更新了老百姓的传统观念。本文通过对黄山市音乐考级现状的调查与分析,了解本市音乐考级这一机制的运行情况,总结音乐考级带来的成效以及存在的不良发展趋向,提出解决弊端的方法并对黄山市音乐考级未来发展提出建设性的建议。     

Children’s Experiences of Completing a Computer‐Based Violence Survey: Ethical Implications

This article aims to contribute to the discussion about the ethics of research on children when studying sensitive issues such as violence. The empirical analysis is based on the accounts given by children (11 377) who completed a computer‐based questionnaire about their experiences of violence (‘The Finnish Child Victim Survey 2008’) and their reflections on the survey. We argue that experiences of violence should not be excluded from the research agenda on children due to ethical reasons, but that a more sophisticated practice should be developed instead to meet the needs and rights of children as informants. © 2010 The Author(s). Journal compilation 2010 National Children’s Bureau and Blackwell Publishing Limited.     

一种基于网格剖分的两点射线追踪方法

两点射线追踪是解决复杂地质结构下反射波射线正演的有效方法。在初至波层析射线追踪算法基础上，提出了一种两点射线追踪方法，利用层析网格对模型进行离散化，通过两阶段法射线追踪，分别对激发点和接收点按向前处理过程计算模型单元及节点的旅行时，两次时间相加作为最小旅行时，再在目的层界面邻域扫描最小旅行时子震源网格单元，用粒子群算法在该单元内扫描全局最小旅行时点，得到反射点位置与反射夹角，再利用向后处理过程运用旅行时线性插值算法，从反射点分别到激发点、接收点反向追踪射线路径，最后得到反射波两点射线路径。     

浅析大学生心理压力的现状与对策

以自编大学生心理压力调查问卷为工具,从大学生对自身心理压力的认知、大学生心理压力产生的根源、大学生心理压力缓解的途径、大学生对心理压力产生原因的认识、大学生对心理压力的归因等方面揭示大学生心理压力的现状,并提出相应的应对策略。     

Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey

Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.