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...     

话语权视阈下双边口译员翻译行为研究

双边口译员的角色或立场描述已从传统观念认为的"隐身""传送带""管道"等转变成"显身""交际促成人""文化斡旋人"等更为宽泛的解释.究其原因,前人所做的研究大都以译员角色为载体,从目的论、话语分析或跨文化交际的角度进行分析.文章则试图以翻译行为为载体,结合口译案例,从话语权的角度对该问题进行阐释,研究结果表明:译员对交际目标的把握是保证话语含意推理正确的关键,其推理结果会产生不同的翻译行为,包括语义行为、语用行为和功能翻译行为.     

网络民族志基于研究者身份的伦理关系

民族志一度是研究者专业生产他者知识的手段与途径,但在反思传统与顺应新潮流——互联网的基础上,我们发现,一直以来研究者自己也是民族志的研究对象,互联网兴起之后,这种倾向更加明显.网络时代的到来促使研究者的身份发生了显著转变:由自己是主体,被研究者是他者,转变为双方是平等的交互主体;由认识主体转变为实践主体;由主动的实践者转变为静默的潜伏者等.网络民族志中研究者身份的变化,为新的研究伦理关系的出现开辟了道路,即研究者与被研究者既是学术研究的手段,也是学术研究的目的.     

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.     

汉代乐舞中的人本主义精神探析

在我国长达3000多年的乐舞文化中,汉代乐舞以其张扬阔大、发扬蹈厉的强悍风格独树一帜,堪称中华乐舞史的最强音.造就强汉之音的精神底座,是在秦汉代际蓬勃兴起的人本主义精神.从汉武时期乐制的更作和乐府制度的确立,到盛行两汉的乐舞形制,再到志尽于诗、音尽于曲的乐舞歌诗和意气相尚、悲歌厉响的审美取向,无不反映并凸显着人本主义精神的光辉.在我国当下民族复兴的进程中,人本主义精神同样应成为时代诉求.     

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.