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...     

政府动态奖惩机制下绿色建筑供给侧演化博弈研究

从绿色建筑供给侧角度出发，构建消费者效用函数、开发商利润函数和政府与开发商的演化博弈模型，分析消费者绿色偏好、建筑绿色度、成本等因素对建筑价格、需求和利润的影响以及政府动态奖惩政策下系统的演化稳定策略。结果表明：消费者绿色偏好支付系数和建筑绿色度的增大有利于绿色建筑价格、需求和利润的提高，但高成本却使需求和利润降低；政府采用静态补贴与静态税收政策时，博弈系统不存在演化稳定策略，采用动态补贴与静态税收、静态补贴与动态税收、动态补贴与动态税收三种政策组合时，系统存在演化稳定策略；动态补贴与静态税收的政策组合在推动绿色建筑发展方面优于其他政策；开发商开发绿色建筑的概率与补贴上限值呈负相关，与税收上限值呈正相关。     

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.     

基于TOPSIS熵权法的城市韧性评价及韧性建设路径研究——以安徽省为例

城市需要足够的韧性以应对复杂多变的外部环境.高质量发展背景下,统筹规划城市韧性建设对城市可持续健康发展具有重要意义.采用TOPSIS熵权法模型测度2011-2018年期间安徽省各地级市韧性评价值,构建韧性城市评价指标体系,分析安徽各城市韧性演化情况.安徽16个地级市的平均韧性水平随时间呈上升趋势,暂不存在区域间城市韧性发展不平衡问题.结合韧性城市建设的总体思路和要求,安徽各城市应不断优化城市的空间结构,增强城市在经济、社会和基础设施等领域的韧性水平.     

1930年代沪上文坛独特的“新感觉”--南洋华侨作家黑婴的“乡愁”书写

史称“新感觉派”后起之秀的黑婴是唯一一位跻身于1930年代中国主流文坛的南洋华侨作家。这位生于荷印棉兰，长于广东梅县，深造于“魔都”上海的新一代南洋华侨，在“民国时态”浓郁的南洋氛围中登陆沪上文坛。在此跨文化的激烈碰撞与深度融合中，黑婴经由“乡愁”的激发与引领，讲述了新一代南洋华侨青年的苦难、迷惘以及艰难的蜕变。今天看来，这种包含着独特生命体验和丰富历史痛感的文化乡愁，正是南侨作家黑婴在“华文文学”和“海派文学”的脉络之外，贡献给中国现代文学真正而独特的“新感觉”。     

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

     

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.     

Constructing Trustworthy Historical Narratives: Criteria,Principles and Techniques

Organizational scholars increasingly recognize the value of employing historical research. Yet the fields of history and organization studies struggle to reconcile. In this paper, the authors contend that a closer connection between these two fields is possible if organizational historians bring their role in the construction of historical narratives to the fore and open up their research decisions for discussion. They provide guidelines to support this endeavor, drawing on four criteria that are prevalent within interpretive organization studies for developing the trustworthiness of research: credibility; confirmability; dependability; and transferability. In contrast to the traditional use of trustworthiness criteria to evaluate the quality of research, the authors advance the criteria to encourage historians to generate more transparent narratives. Such transparency allows others to comprehend and comment on the construction of narratives, thereby building trust and understanding. Each criterion is converted into a set of guiding principles to enhance the trustworthiness of historical research, pairing each principle with a practical technique gleaned from a range of disciplines within the social sciences to provide practical guidance.