对学习历史事件法的一种改进

依据组织学习理论,对学习历史事件法进行了改进。由于企业的经验知识不完全等同于隐性知识,学习历史事件法同样不能直接应用于隐性知识的转化,为此,提出了适合隐性知识转化的改进型学习历史事件法。改进充分考虑了隐性知识的特征,并专门对隐性知识和经验知识进行了区分。同时,改进的学习历史事件法充分考虑了企业应用的可操作性,保留了量表的形式。最后,将该方法应用于MIS项目开发中,对新改进的量表进行了定量解析。     

Conditional and marginal estimates in case-control family data – extensions and sensitivity analyses

This work considers two specific estimation techniques for the family-specific proportional hazards model and for the population-averaged proportional hazards model. So far, these two estimation procedures were presented and studied under the gamma frailty distribution mainly because of its simple interpretation and mathematical tractability. Modifications of both procedures for other frailty distributions, such as the inverse Gaussian, positive stable and a specific case of discrete distribution, are presented. By extensive simulations, it is shown that under the family-specific proportional hazards model, the gamma frailty model appears to be robust to frailty distribution mis-specification in both bias and efficiency loss in the marginal parameters. The population-averaged proportional hazards model, is found to be robust under the gamma frailty model mis-specification only under moderate or weak dependency within cluster members.     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

It's Complicated: The 2014–2015 U.S. Measles Outbreak and Parents’ Vaccination Beliefs,Confidence, and Intentions

While it seems intuitive that highly visible vaccine‐preventable disease outbreaks should impact perceptions of disease risk and facilitate vaccination, few empirical studies exist to confirm or dispel these beliefs. This study investigates the impact of the 2014–2015 Disneyland measles outbreak on parents’ vaccination attitudes and future vaccination intentions. The analysis relies on a pair of public opinion surveys of American parents with at least one child under the age of six (N = 1,000 across each survey). Controlling for basic demographics, we found higher levels of reported confidence in the safety and efficacy of childhood vaccinations in our follow‐up data collection. However, this confidence was also accompanied by elevated levels of concern toward childhood vaccines among American parents. We then examined how different subgroups in the population scored on these measures before and after the outbreak. We found that parents with high levels of interest in the topic of vaccines and a child who is not fully upto date with the recommended vaccination schedule reported more supportive attitudes toward vaccines. However, future intentions to follow the recommended vaccination schedule were not positively impacted by the outbreak. Possible explanations for these results and implications for vaccination outreach are discussed.     

A class of partially linear transformation models for recurrent gap times

In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.     

历史文献学视野下的重大历史事件

重大历史事件的提出源于国家文艺创作政策,是重大历史题材的内核。从历史文献学的视角来看,重大历史事件的文献形态表现为本源事件、衍生事件和文本事件。重大历史事件的衍生特征主要体现为本源事件的内容选择性、视角多样性、衍生多态性和历史叙事的结构层次性。重大历史事件具有文献能产性,其根本动力来源于历史主体的社会认可需要、纪念活动的客观物证需要和文艺创作的传奇叙事需要。     

No Expo Network: a failed mobilization in a post-political frame

In 2015 Milan hosted the Universal Exposition with the theme ‘Feeding the Planet, Energy for Life’. Even if characterized by various scandals and problems, the edition was narrated by the mainstream media as a political and economic success. Critical voices were almost completely obscured by favorable propaganda and the ideas of development and the future proposed by the Expo rhetoric was presented as inevitable, configuring ‘the best of all possible worlds’ in a more general post-political frame. In this profile I first present the main characteristics of the No Expo Network, e.g. the actors that composed it and the main critiques that they advanced. I will then focus on the reasons for its defeat, which is then contextualized in relation to the election of the Chief Executive Officer of Expo 2015 as new Mayor of Milan. Here, we can see the continuation and structural strengthening of the neoliberal politics of Expo2015 beyond the mega event itself.     

中国决策情境下的多源流模型及其优化研究

作为当今公共政策研究领域最炙手可热的理论模型之一,约翰·金登的多源流分析理论业已得到国内外学者的广泛应用与认可。在对该模型的相关内容进行简单铺陈后,通过辨析国内外不同的政策主体参与逻辑,进一步厘清了我国公共决策的独有特征。研究重点是在整合提炼西方既有修正思路的基础上,沿袭模型的模糊性假设,结合我国具体实践案例,对多源流模型进行优化与补充。研究表明,中国决策情境下的多源流模型优化主要有提高源流间的融合性、政治源流的核心化、焦点事件的独立化以及特殊情况下的临时决策等四种路径,四种优化与补充路径的有机结合将为进一步提升模型适用性提供帮助。     

Optimal cut-off for rare events and unbalanced misclassification costs

This paper develops a method for handling two-class classification problems with highly unbalanced class sizes and misclassification costs. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional classification methods tend to strongly favour the majority class, resulting in very low detection of the minority class. A method is proposed to determine the optimal cut-off for asymmetric misclassification costs and for unbalanced class sizes. Monte Carlo simulations show that this proposal performs better than the method based on the notion of classification accuracy. Finally, the proposed method is applied to empirical data on Italian small and medium enterprises to classify them into default and non-default groups.     

Using Multilevel Statistical Models in Social Work Intervention Research

ABSTRACT

Statistical analyses of data from a classroom-based study illustrate the need to account for intra-class clustering in studies involving schools, classrooms, and other higher order units of analysis. Students were clustered in homerooms that were assigned to intervention and comparison conditions. Standard multiple linear regression analysis yielded a significant group effect but incorrectly ignored intra-cluster response correlations. A multilevel model appropriately accounting for the dependency among responses in the same cluster yielded a nonsignificant group effect. Implications for the analysis of intervention research data are discussed.