谈网络时代的高校思想政治教育课

在经济全球化、网络普及化的今天,如何培养同中国特色社会主义现代化要求相适应的高素质合格人才,发挥我国巨大人才资源的优势,这关系到21世纪社会主义事业的全局。为此,高校思想政治教育课必须努力走出传统教学模式,突出＂实践教学＂环节,发挥思想政治教育课的积极作用。     

中英古诗中的爱情主题纵论

以爱情中的情感的发展历程为视角,通过对英国文艺复兴时期与中国古代文学中一描写爱情的诗歌对比分析,展现了一幅风格迥异又纵横交错的爱情世界的全景图.研究发现中英古诗中都包含大量讴歌爱情的激情诗句,但由于作者彼此情感经历的不同、中西文化差异等因素使二者在内容与形式的表述上各放异彩.     

特色产业投资对西部县域经济发展的作用机理研究

近年来,西部县域经济有了一定的发展,但其发展速度缓慢.加快西部县域经济发展,必须将有限的资金优先投资于特色产业,使特色产业成为县域经济快速增长的突破口,推动县域经济进入良性循环发展轨道.     

论先秦名辩思潮中的语言功能观

语言的功能是语言哲学研究的基本问题。先秦"名辩思潮"中的先秦诸子语言功能观显现为两个层面:一是语言的社会实践功能;二是语言的逻辑功能。以孔子为代表的"以名正实"的名实观中,语言体现出明显的社会实践功能;道家的"无名"论、荀子的"制名以指实"思想,以及墨家的"实名"思想则分别体现出语言在表征"道"、表述"共相"和指称"实体"方面所具有的逻辑功能。     

大数据环境下马克思主义大众化路径探析——以高校马克思主义大众化为例

从马克思主义技术哲学的视野来看,大数据的本质是人的实践活动,其引发了人们认识方式的变革,具有不可估量的价值.大数据与马克思主义大众化在科学性、人民性、实践性方面的紧密契合,决定它必将推进马克思主义大众化的进程.充分运用大数据正确认识高校马克思主义大众化的现状,创新马克思主义大众化路径.通过不断完善高校马克思主义大众化的大数据环境,推进高校马克思主义大众化的深入发展.     

Old Voters on New Dimensions: Why Do Voters Vote for Pensioners’ Parties? The Case of the Netherlands

This article analyzes the electoral support of the Dutch pensioners’ party 50Plus. Due to its open electoral system and aging population, the Netherlands is a key case to study pensioners’ parties. Our study shows that this pensioners’ party appeals to voters who are characterized by their age and their dependence on the welfare state as well as their policy positions on new lines of political conflict. In particular, their position on the new economic dimension (which concerns welfare state reform) and the new cultural dimension (which concerns immigration and EU integration) is distinct. Moreover, even when the majority of voters for this new party once supported the larger mainstream parties, they are now dissatisfied with the established politics. With rapidly aging populations across established democracies, this study is not just relevant for those studying pensioners’ parties, but rather gives an important insight into the electoral dynamics and popular support for mainstream politics, the welfare state, and social security.     

Geospatial Indicators of Space and Place: A Review of Multilevel Studies of HIV Prevention and Care Outcomes Among Young Men Who Have Sex With Men in the United States

Young men who have sex with men (YMSM), particularly YMSM who are racial/ethnic minorities, are disproportionately affected by the human immunodeficiency virus (HIV) epidemic in the United States. These HIV disparities have been linked to demographic, social, and physical geospatial characteristics. The objective of this scoping review was to summarize the existing evidence from multilevel studies examining how geospatial characteristics are associated with HIV prevention and care outcomes among YMSM populations. Our literature search uncovered 126 peer-reviewed articles, of which 17 were eligible for inclusion based on our review criteria. Nine studies examined geospatial characteristics as predictors of HIV prevention outcomes. Nine of the 17 studies reported HIV care outcomes. From the synthesis regarding the current state of research around geospatial correlates of behavioral and biological HIV risk, we propose strategies to move the field forward in order to inform the design of future multilevel research and intervention studies for this population.     

Analysis of a marker for cancer of the thyroid with a limit of detection

Limit of detection (LoD) is a common problem in the analysis of data generated by instruments that cannot detect very small concentrations or other quantities, resulting in left-censored measurements. Methods intended for data that are not subject to this problem are often difficult to modify for censoring. We adapt the simulation-extrapolation method, devised originally for fitting models with measurement error, to dealing with LoD in conjunction with a mixture analysis. The application relates the levels of thyroglobulin in individuals with cancer of the thyroid before and after treatment with radioactive iodine I–131. We conclude that the fitted mixture components correspond to levels of effectiveness of the treatment.     

Simulation-based model comparison methodology with application to road accident models

This article assumes the goal of proposing a simulation-based theoretical model comparison methodology with application to two time series road accident models. The model comparison exercise helps to quantify the main differences and similarities between the two models and comprises of three main stages: (1) simulation of time series through a true model with predefined properties; (2) estimation of the alternative model using the simulated data; (3) sensitivity analysis to quantify the effect of changes in the true model parameters on alternative model parameter estimates through analysis of variance, ANOVA. The proposed methodology is applied to two time series road accident models: UCM (unobserved components model) and DRAG (Demand for Road Use, Accidents and their Severity). Assuming that the real data-generating process is the UCM, new datasets approximating the road accident data are generated, and DRAG models are estimated using the simulated data. Since these two methodologies are usually assumed to be equivalent, in a sense that both models accurately capture the true effects of the regressors, we are specifically addressing the modeling of the stochastic trend, through the alternative model. Stochastic trend is the time-varying component and is one of the crucial factors in time series road accident data. Theoretically, it can be easily modeled through UCM, given its modeling properties. However, properly capturing the effect of a non-stationary component such as stochastic trend in a stationary explanatory model such as DRAG is challenging. After obtaining the parameter estimates of the alternative model (DRAG), the estimates of both true and alternative models are compared and the differences are quantified through experimental design and ANOVA techniques. It is observed that the effects of the explanatory variables used in the UCM simulation are only partially captured by the respective DRAG coefficients. This a priori, could be due to multicollinearity but the results of both simulation of UCM data and estimating of DRAG models reveal that there is no significant static correlation among regressors. Moreover, in fact, using ANOVA, it is determined that this regression coefficient estimation bias is caused by the presence of the stochastic trend present in the simulated data. Thus, the results of the methodological development suggest that the stochastic component present in the data should be treated accordingly through a preliminary, exploratory data analysis.