Co-constructing a Conceptual Understanding of System Enactment

Clinical Social Work Journal - System enactments are co-created phenomena characterized by confounding and emotionally charged multi-person interactions that emerge through the convergence of...     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Efficacy Foundations for Risk Communication: How People Think About Reducing the Risks of Climate Change

Believing action to reduce the risks of climate change is both possible (self‐efficacy) and effective (response efficacy) is essential to motivate and sustain risk mitigation efforts, according to current risk communication theory. Although the public recognizes the dangers of climate change, and is deluged with lists of possible mitigative actions, little is known about public efficacy beliefs in the context of climate change. Prior efficacy studies rely on conflicting constructs and measures of efficacy, and links between efficacy and risk management actions are muddled. As a result, much remains to learn about how laypersons think about the ease and effectiveness of potential mitigative actions. To bring clarity and inform risk communication and management efforts, we investigate how people think about efficacy in the context of climate change risk management by analyzing unprompted and prompted beliefs from two national surveys (N = 405, N = 1,820). In general, respondents distinguish little between effective and ineffective climate strategies. While many respondents appreciate that reducing fossil fuel use is an effective risk mitigation strategy, overall assessments reflect persistent misconceptions about climate change causes, and uncertainties about the effectiveness of risk mitigation strategies. Our findings suggest targeting climate change risk communication and management strategies to (1) address gaps in people's existing mental models of climate action, (2) leverage existing public understanding of both potentially effective mitigation strategies and the collective action dilemma at the heart of climate change action, and (3) take into account ideologically driven reactions to behavior change and government action framed as climate action.     

中国员工企业伦理态度调查及分析

随着中国市场经济的发展,各种企业丑闻的频发引起业界广泛关注,公司治理越来越受到重视。已有的研究表明,员工企业伦理态度关乎员工承诺度和企业竞争力。本文依据个人层次研究理论,采用实证分析方法,选择国内6家企业的员工为调研对象,沿用国外企业伦理个体层次研究中广泛使用的量表"Attitude Towards Business Ethics Questionnaire"(ATBEQ)即企业伦理态度量表,考察当代中国企业员工对企业伦理态度的看法;另外,也分析了不同的工作年限,性别,学历,户口等因素下的企业员工伦理态度差异。调查结果显示:中国员工的企业伦理观存在困惑;大部分中国员工有企业利益相关者意识;教育对企业员工伦理态度存在显著影响。     

1950年代:“本质—特征论”文学定义的形成及影响

1950年代形成了"本质—特征论"的文学定义,即先界定文学的意识形态本质,再界定文学是用形象的方式来反映社会生活、体现意识形态的特征。这一定义的思维方式是先界定事物的普遍性,再界定事物的特殊性,突出事物的普遍性关联,轻视事物的独特性质的揭示,使得文学研究本应着重研究文学的特殊性成为空话,不利于认识文学自身的规律。1980年代的文学审美论试图突破这种定义方式,但1990年代以来,反本质主义的文学语境论再次回到从事物的关联性定义事物的思维老路,文学自身本质问题受到质疑,弱化了文学与审美关联的重要性与必要性。     

Feminist and empowerment theory and social work practice

Feminist and empowerment theories are especially important to the understanding of individual and sociopolitical levels of social work assessment and intervention. Incorporating feminist and empowerment approaches in practice will provide social workers with the knowledge, values and skills most likely to promote human rights and social justice. In this paper, we present an overview of both theories and illustrate them with a case example.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

高含水期油藏层间压力结构差异量化方法研究

由于长期注水开发和层间非均质性影响，高含水期油藏各小层层间压力结构差异严重，掌握差异状况是合理调整的重要依据。针对现场分层测压资料少，单层压力资料获取困难的情况，应用渗流力学原理建立了高含水期地层层间压力结构差异量化数学模型，模型的关键参数可以通过生产数据计算获得。枣园油田的实例验证表明，高含水油藏层间压力存在明显差异，该方法能够确定各小层的压力差异状况。量化计算结果和实测资料相接近，有利于认清开发矛盾，为合理调整提供技术依据。