Reframing Resilience: Equitable Access to Essential Services

We urgently need to put the concept of resilience into practice if we are to prepare our communities for climate change and exacerbated natural hazards. Yet, despite the extensive discussion surrounding community resilience, operationalizing the concept remains challenging. The dominant approaches for assessing resilience focus on either evaluating community characteristics or infrastructure functionality. While both remain useful, they have several limitations to their ability to provide actionable insight. More importantly, the current conceptualizations do not consider essential services or how access is impaired by hazards. We argue that people need access to services such as food, education, health care, and cultural amenities, in addition to water, power, sanitation, and communications, to get back some semblance of normal life. Providing equitable access to these types of services and quickly restoring that access following a disruption are paramount to community resilience. We propose a new conceptualization of community resilience that is based on access to essential services. This reframing of resilience facilitates a new measure of resilience that is spatially explicit and operational. Using two illustrative examples from the impacts of Hurricanes Florence and Michael, we demonstrate how decisionmakers and planners can use this framework to visualize the effect of a hazard and quantify resilience-enhancing interventions. This “equitable access to essentials” approach to community resilience integrates with spatial planning, and will enable communities not only to “bounce back” from a disruption, but to “bound forward” and improve the resilience and quality of life for all residents.     

No Mind Reading Necessary: Conducting Evidence-Based Electronic Resource Marketing and Outreach Using Marketing Research Assessment

Abstract

In her presentation entitled “No Mind Reading Necessary: Conducting Evidence-Based Electronic Resource Marketing and Outreach” at the North Carolina Serials Conference April 2019, Kate Hill discussed marketing research strategies and outreach tools used to increase online users’ access to electronic resources and primary resources. In an effort to connect users to seldom-used resources, Jackson Library collaborated with university partners to develop surveys to identify the information-seeking behavior of its users and apply the gathered marketing research to strategically plan outreach activities.     

Resistance to metropolitan institutionality and planning in Chile

In this article, the authors review and analyse two key processes conducted by the Chilean state over the past 50 years. The first process consists of the development of specific planning instruments for the particular realities of metropolitan areas. The second process consists of the successive legislative attempts to work towards a definition of a new form of institutionality for cities with metropolitan profiles. These attempts have either failed or solely become bills of law. Both processes suggest a political and technical resistance throughout history, to substantially modify institutionality, as well as planning instruments, in order to make them more appropriate and consistent with the needs of growing metropolitan areas in Chile.     

日本新订高中历史课程之特点与启示

2018年3月,日本发布新的《高等学校学习指导要领》。这是日本自第二次世界大战后70多年来,对高中课程最大规模的一次修订,体现了日本高中教育发展的新趋势,标志着日本从此告别“宽松教育”而走向“生存能力”教育。由“历史综合”“日本史探究”“世界史探究”组成的新高中历史课程,立足“人”的发展,构建了“必修+选修、通史+专题”的课程体系,课程内容更加突出本国史与世界史的联系,课程学习倡导“主题+探究”的学习方式。日本高中历史课程的内容设置及特点启示我们,高中历史课程编制应加强中国史与世界史的横向关联,课程内容应体现探究性学习要求。     

机器学习视角下中国去产能产业的识别

随着中国环境压力的增加,政府提出了供给侧改革,去产能是改革的主要内容,但是由于产业特征的实时演变,需要对政策进行完善。文章运用模糊C均值算法和支持向量机算法分析现阶段需要进行去产能的产业,结果发现在现行去产能政策中大部分行业是需要去产能的,但煤炭开采和洗选业以及铁路、船舶、航空航天和其他运输设备制造业已不适合继续去产能,同时将化学原料和化学制品制造业加入去产能行列中。     

Task-based learning approaches for supporting the development of social science researchers’ critical data skills

Conducting social science research is a complex process, and social science students face a number of learning challenges in order to develop their skills. Moreover, new sources and types of data including so-called big data are providing new opportunities for research, but also pose methodological challenges. In this article, we explore a task-based learning approach for teaching social science research methods. We draw on evidence from two case study learning tasks: (i) the collection and analysis of Twitter data; and (ii) designing and conducting a face-to-face and online survey. The students were guided to co-lead the tasks, apply their knowledge and then to critically reflect. The pedagogical framework of task-based learning provides opportunities to embed learning in new ways by integrating knowledge, practice and critical reflection. Task-based learning can create a dynamic learning environment and can empower students to develop their identities as researchers.     

Older Adults’ Expectations about Mortality,Driving Life and Years Left without Driving

ABSTRACT

People are living and driving longer than ever before, with little preparation for transitioning to being non-drivers. We investigated driving expectations among drivers age 65 and older, including sociodemographic and driving context predictors. Cross-sectional data from 349 older drivers were explored to determine variation in how many years they expected to continue driving. General linear models examined predictors of both expectations. In this predominantly Black/African American sample, 76% of older drivers (mean age = 73 ± 5.7 years) expected a non-driving future, forecasting living an average of 5.75 ± 7.29 years after driving cessation. Regression models on years left of driving life and years left to live post-driving cessation predicted nearly half of the variance in older drivers’ expectations with five significant predictors: income, current age, age expected to live to, self-limiting driving to nearby places and difficulty, visualizing being a non-driver. Many older drivers expect to stop driving before end of life.     

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.