现代本质主义的逻辑基础与哲学意蕴

<正>现代分析哲学家致力于意义的分析,力图通过对语言的分析使哲学科学化。以罗素和逻辑实证主义为代表的前期分析哲学家拒斥一切形而上学,所有导致本质主义的学说均被他们宣布为虚妄。但到     

论技治主义:以凡勃伦为例

<正>20世纪下半叶以来,技治主义所主张的政治实践科学化已经成为全球范围内政治活动的显著趋势。美国在罗斯福新政之后,社会管理、公共管理和政府治理日益成为"某种技术性事务",技治主义跻身主流意识形态,地位堪比实用主义。(Kleinberg,p.4)技治主义还广泛传入其他国家,产生     

元认知在画图表征策略和小学生数学问题解决能力中的中介作用

该研究采用自然实验和问卷调查法,对268名小学五年级学生进行画图表征策略训练,在此基础上根据策略学习情况筛选出掌握了该策略的218名学生,以这些学生为被试,探讨元认知在画图表征策略和小学生数学问题解决能力中的作用。结果表明:(1)策略学习后,小学生的画图表征策略水平和数学问题解决成绩均显著高于策略学习前。(2)画图表征策略通过元认知这一中介变量对小学生数学问题解决能力有显著的促进作用。     

个体对效价预期和风险评估的时程加工机制:来自ERP的证据

采用事件相关电位技术(ERP),通过简单赌博任务,操纵结果预期的效价(输或赢)与风险(高风险与低风险),考察了个体对结果预期阶段的效价和风险评估的脑内时程动态加工过程。脑电结果发现:(1)在N2成分上,结果预期的效价主效应显著,预期输比预期赢能够引起更大的N2波幅;结果预期的风险主效应不显著。(2)在N500成分上,效价与风险之间的交互作用边缘显著,进一步简单效应分析发现,在预期输的条件下,肯定会输的N500波幅显著大于可能会输;而在预期赢的条件下,肯定会赢与可能会赢之间的N500波幅差异不显著。这说明,在不确定决策过程中的结果预期阶段,个体可能先对结果预期的效价进行加工,然后再对风险进行评估。     

论亚当·斯密的宗教市场理论——兼论宗教管制的经济基础

亚当·斯密的宗教市场理论主要包括宗教理性选择理论、宗教自由放任理论和国际宗教管制理论。亚当·斯密的国际宗教管制理论至今仍不失其科学性与合理性。宗教人对来世的追求表明宗教经济人理性选择理论偏离了人的实际;缺乏道德感宗教的客观存在表明宗教并不当然具有道德教化功能;宗教市场的自律调节功能具有有限性,产品市场自身无法克服的诸多弊端同样存在于宗教市场,取消一切宗教管制的宗教自由放任理论不成立,宗教管制因此具有深刻的经济基础,亚当·斯密的宗教市场理论为宗教管制提供了必要的理论基础。     

从“王贝事件”看医学学会组织的职能

2010年发生于武汉的超女王贝整容致死事件,使中国的医疗整形美容行业受到质疑.相关医学学会组织的职能缺失是事件引起社会关注的重要原因之一.通过梳理该事件发生发展的全过程,分析医学学会的作用,借以指出医学学会在制度建设和职业精神建设等方面存在的问题,并提出完善医学学会职能的建议.     

宗教与全球发展:一种对话路径

宗教与全球发展成为当代社会的一个重要议题,一个标志在于联合国千禧年发展目标的提出及世界宗教和精神领袖千禧年和平高峰会议。其中,由前世界银行行长及当时的坎特伯雷大主教联合发起的世界信仰发展对话,即为推动类似目标的一个重要机构。发展的目的在于人。贫困不仅包括物质的层面,也包括精神的层面。全球经济的发展需要一种伦理和价值的支撑。文化应该作为一种创新机制,参与并影响全球发展的过程。在中国,宗教是建设社会主义和谐社会的重要因素。中共十七大明确指出,要发挥宗教在社会经济发展中的重要作用。十七届六中全会则明确提出文化体制改革的目标。随着中国改革开放的进一步深入,中国对世界的影响日益加强,文化和精神的维度在社会发展和国家建设方面将扮演更重要的作用。中国模式也将对世界论坛的讨论产生一定影响。     

More Than Two Choices: The Influence of Context on the Framing Effect

The framing effect demonstrates that two options with different risk levels affect people??s risk preferences, even if both options are logically equivalent. However, in reality we are almost always confronted with more than two choices, rather than a single manipulated message. In this paper, we simultaneously manipulate the framing effect and the context effect, including the attraction effect and the compromise effect, to examine the influence of context on framing. The results of two studies show that context influences the original choice preference arising from the framing effect. Moreover, the framing effect is reduced when context effects are introduced to the original choice set.     

The effects of self-brand connections on responses to brand failure: A new look at the consumer–brand relationship

We argue that consumers with high self-brand connections (SBC) respond to negative brand information as they do to personal failure — they experience a threat to their positive self-view. After viewing negative brand information, high (vs. low) SBC consumers reported lower state self-esteem. Consumers with high SBC also maintained favorable brand evaluations despite negative brand information. However, when they completed an unrelated self-affirmation task, they lowered their brand evaluations the same as low SBC consumers. This finding suggests that high SBC consumers' reluctance to lower brand evaluation might be driven by a motivation to protect the self rather than the brand.     

Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.