高校哲学社会科学研究评价的本质要求及其制度建设

建设具有中国特色、中国风格、中国气派的哲学社会科学,一直是我国哲学社会科学繁荣发展的根本目的.提升高校哲学社会科学繁荣发展水平,离不开科学有效的哲学社会科学研究评价.坚持党的领导、"二为"方向和"双百"方针是哲学社会科学繁荣发展的根本保障,坚持"质量为本"和"创新至上"是哲学社会科学繁荣发展的必然要求,坚持科学权威和公开透明是哲学社会科学研究评价的现实需要.结合当前高校哲学社会科学研究评价中"量化评价下数量与质量的关系论辩","学术评价与科研管理评价的边界异化"和"学术权力和行政权力的制度规制"三大问题聚讼,文章建议通过强化学术成果质量保障机制、优化学术共同体工作机制、构建学术争鸣长效机制和完善学术成果转化应用机制等制度建设,推动构建科学权威、公开透明的哲学社会科学研究评价体系,以期进一步改善新时代高校哲学社会科学研究评价工作.     

南京中山植物园游憩功能满意度评价研究

随着植物园"旅游经济"的发展,植物园在承担植物科学研究及科普功能的同时,也成为公众日常休闲的重要绿地。本文选取南京中山植物园作为研究对象,建立评价体系并运用模糊综合评价法对游客满意度进行研究,探寻影响植物园游憩功能的显著因子,为未来植物园规划设计提供参考。结果表明:游客对南京中山植物园的游憩功能达到基本满意水平。其中,植物物种多样性、景区分布、建筑景观、声景观的满意度较高,滨水景观的满意度评价值较低。通过Pearson相关系数发现植物色彩丰富度、科普场所、科普内容是影响游客满意度的显著因子。运用IPA图对16项评价因子进行重要度-满意度分析,并对各象限内的因子提出改进方向。最后基于游憩功能满意度评价结果,提出改进策略:加大对植物园休息设施、卫生设施、娱乐设施和商业服务设施的投入;更新科普方式,增添特色游憩主题园区;完善现有道路体系,增强对北部专类园的可达性;增加色叶植物的种类、提高色彩丰富度等。以期为中山植物园游憩功能的优化及其他植物园的规划设计提供借鉴。     

货币政策反应函数渠道对金融周期影响时变机制的实证研究

在关于货币政策影响经济主体风险承担水平,进而影响金融周期波动机制的研究中,基于风险承担渠道的相关研究较为成熟.区别于以往相关研究多关注货币政策实际采取的立场,文章基于货币政策反应函数渠道探讨了数量型与价格型货币政策反应函数对金融周期波动影响的时变机制.滚动回归的实证结果显示:无论数量型货币政策规则还是价格型货币政策规则,货币政策对信贷波动反应的敏感性主要影响金融周期的波动,但在价格型货币政策规则下,基于信贷视角观察金融周期波动时,货币政策信贷敏感性与货币政策资产价格敏感性对金融周期影响差异较小;较之于价格型货币政策规则,货币政策对信贷波动反应的敏感性在数量型货币政策规则下,对金融周期波动的影响更显著,并在一定程度上表现出随时间扩大的趋势.文章的创新之处在于:强调了货币政策通过政策反应函数渠道而非以往研究中较多关注的狭义风险承担渠道影响金融周期波动的事实,并构建计量模型对货币政策反应函数渠道影响金融周期波动的时变机制进行了详细刻画.     

人口流动导引的家庭代价及发展省思—基于陇东南 Q 村的个案考察

以陇东南 Q 村为个案,在分析农村人口流动动因的基础上,系统梳理了人口流动导引的一系列家庭代价。 家庭功能遭致消解性代价方面,表现为儿童青少年的教育抚养和老年人养老照料功能弱化;家庭风险多发频发性代价方面,表现为婚姻和家庭稳定性下降、青少年越轨行为突出和家庭成员安全风险增大;家庭分化或衰落性代价方面,表现为空巢家庭、隔代家庭和独身家庭大量出现,家庭的空壳化和复杂分化成为部分家庭走向衰落的前兆。 代价视角的分析表明,需要将家庭置于相关制度设置和公共政策讨论的核心,明确家庭建设在中国农村社会具有的特殊的文化价值和现实意义。 发展和守护家庭,调动家庭自我保障、自我调节和服务的功能及积极性,是政府相关政策制定中必须予以重视的关键议题。     

Improved two-parameter estimators for the negative binomial and Poisson regression models

Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. We introduce new two-parameter estimators (TPEs) for the NBR and the PR models by unifying the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [The restricted and unrestricted two-parameter estimators. Commun Stat Theory Methods. 2007;36:2707–2725]. These new estimators are general estimators which include maximum likelihood (ML) estimator, ridge estimator (RE), Liu estimator (LE) and contraction estimator (CE) as special cases. Furthermore, biasing parameters of these estimators are given and a Monte Carlo simulation is done to evaluate the performance of these estimators using mean square error (MSE) criterion. The benefits of the new TPEs are also illustrated in an empirical application. The results show that the new proposed TPEs for the NBR and the PR models are better than the ML estimator, the RE and the LE.     

On the estimation of non linear functions in stochastic volatility models

Abstract

This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.     

Kernel density estimation under negative superadditive dependence and its application for real data

In this paper, the kernel density estimator for negatively superadditive dependent random variables is studied. The exponential inequalities and the exponential rate for the kernel estimator of density function with a uniform version, over compact sets are investigated. Also, the optimal bandwidth rate of the estimator is obtained using mean integrated squared error. The results are generalized and used to improve the ones obtained for the case of associated sequences. As an application, FGM sequences that fulfil our assumptions are investigated. Also, the convergence rate of the kernel density estimator is illustrated via a simulation study. Moreover, a real data analysis is presented.     

中国自主品牌形象提升的关键驱动因素及对策研究——基于矫形与塑形传播的视角

提升自主品牌形象是当前改善自主品牌消费环境、扩大自主品牌消费市场的重要现实命题,而厘清自主品牌负面刻板印象生成的根源及影响因素是探讨自主品牌形象提升的关键驱动因素与战略对策的理论基础。首先以8个论坛的受众跟帖为例,运用扎根理论进行探索性研究,发现负面企业形象、负面国货形象、负面政府形象、媒体渲染、国货意识淡漠和社会规范压力等6个主范畴是导致自主品牌负面刻板印象生成的主要影响因素;然后将范畴作为测量问项,将主范畴作为潜变量,进行问卷调查和实证分析,结果表明,6个主范畴均对自主品牌负面刻板印象存在显著的正向影响。在此基础上,根据自主品牌负面刻板印象生成的影响因素,提炼出自主品牌形象提升的5大关键驱动因素,即企业形象驱动因素、政府形象驱动因素、媒介框架驱动因素、消费者情感驱动因素和社会规范驱动因素,并对其影响效应及对策措施进行了探讨,建构了自主品牌形象提升的关键驱动因素影响效应模型。