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。     

核技术应用企业研发骨干激励机制探究

核技术应用一般是指射线技术与射线装置在医疗、工农业、地质调查、研究与教育中的应用。我国核技术应用企业研发骨干激励存在着激励的创新导向不足、薪酬激励机制不合理、组织文化有欠缺等问题,需结合行业特点及核技术应用企业研发骨干激励的特殊要求,采取相应的激励对策,包括从核心能力出发研究激励问题、建立适于激励研发人员的能力工资制、注重职业发展激励、注重责任激励、建立激发创新活力的组织文化等。     

大众传播中的“国家教师”媒介形象分析

运用传播学内容分析法，以中央媒体对获得"全国教书育人楷模"和"最美教师"荣誉称号的"国家教师"相关报道为研究对象，从外在和内在两个方面对大众传播的"国家教师"媒介形象进行分析。外在形象包括教师性别、年龄、教龄、外表、场景以及任教的学校类别、区域、学科专业等方面，内在形象包括职业精神、职业知识和职业能力等方面。研究发现，大众媒体对"国家教师"形象的刻画和宣传符合社会公众认知教师职业的基本逻辑，起到了传递教师精神、端正教师形象、唤起社会尊重教师的功效，但也存在城乡教师形象传播刻板化以及教师普通人形象展现不充分等问题，可在后续的传播中对"国家教师"形象的价值倾向进行适当调整。     

Non-parametric estimation of reciprocal coordinate subtangent for right censored dependent scheme

The concept of reciprocal coordinate subtangent (RCST) has been used as a useful tool to study the monotone behavior of a continuous density function and for characterizing probability distributions. In this paper, we propose a non-parametric estimator for RCST based on the censored dependent data. Asymptotic properties of the estimator are established under suitable regularity conditions. A simulation study is carried out to examine the performance of the estimator. The usefulness of the estimator is also examined through a real data.     

丑角、看客与孤独者的悲喜剧 ——鲁迅与穆时英笔下的丑角形象之比较

鲁迅与穆时英笔下都塑造过一系列具有丑角特质的形象。不同的是,穆时英笔下的丑角具有自觉性,以小丑身份为面具,掩盖自己的孤独与恐惧；鲁迅笔下的丑角多是非自觉的,他们认真地践行着自己的人生理念,却因其与周遭环境格格不入,被人当作小丑、疯子。从中折射出两位作家迥异的生命观,穆时英从个体角度出发,写出了都市人在悲哀的脸上戴了快乐面具的无奈,对丑角表演持拥抱态度；鲁迅则从批判国民性的宏观角度出发,敏锐地意识到了丑角表演背后的隐忧,对其持拒斥的姿态。     

双循环新发展格局下中国双向直接投资研究

构建以国内大循环为主体、国内国际双循环相互促进的新发展格局,是对未来中国经济发展路径做出的重大战略部署,作为连接国内国际双循环的纽带,双向直接投资的发展战略和路径面临重塑和调整。研究认为,应当以推动新时代科技创新和产业升级为目标,提高双向直接投资的质量,畅通供应链和产业链; 以“一带一路”倡议为依托,通过更高水平的双向投资带动中国与沿线国家贸易水平的提高; 建设高水平自贸区和自贸港,提高对外开放标准,更高质量吸引外资,提升参与国际大循环的技术含量和自主性; 在畅通要素流动领域,建立更加完善的现代化物流体系,建设更高标准的要素流通市场体系,降低制度性交易费用,提高生产率和对高质量外资的吸引能力。     

Value drivers of social businesses: A business model perspective

Hybrid organizational forms that combine commercial and welfare institutional logics play an increasingly important role in addressing the grand societal challenges we face today. Building on the literatures on hybrid organizations and social business models, we explore the characteristics of social businesses from a business model perspective. This study seeks to better understand the particularities and value drivers of hybrid social purpose in contrast to purely commercial business models. We follow a grounded theory approach and our findings are based on interview data from 17 social business firms. Building on social businesses' identified particularities, we propose four value drivers of social business models: 1) responsible efficiency, 2) impact complementarities, 3) shared values, and 4) integration novelties. We link our findings to the literature, contributing new insights into social businesses models and implications for practitioners.     

Institutionalized collective action and the relationship between beliefs about environmental problems and environmental actions: A cross-national analysis

How do institutions affect the relationship between an individual's beliefs and their actions? Institutionalized strategies are routine ways of addressing problems that become taken-for-granted in a society. Environmental problems constitute a collective action problem in that personal consumption often conflicts with collective interests. I test whether beliefs about environmental problems have a different impact on a person's pro-environmental behaviors, depending on how addressing collective action problems is institutionalized in their society. In particular, I use level of welfare targeting as an observable, organizational difference among societies that reflects different institutionalized strategies for addressing a prominent collective action problem. I use multilevel models on data from the 2010 International Social Survey Programme (ISSP) and measures of welfare targeting from the Organization for Economic Cooperation and Development (OECD) to answer this question. I find that in societies where the institutionalized strategy for dealing with inequality is highly targeted, individuals' beliefs that these problems are important, real, and whether they can do something about them have a greater impact on their actions. The results suggest individuals generalize taken-for-granted strategies of assuring collective welfare to implement their individual beliefs about the environment, making institutional environments important moderators of the strength of the belief-action relationship.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.