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.     

Cooperative innovation through a talent management pool: A qualitative study on coopetition in healthcare

In recent years, the Dutch healthcare sector has been confronted with increased competition. Not only are financial resources scarce, Dutch hospitals also need to compete with other hospitals in the same geographic area to attract and retain talented employees due to considerable labour shortages. However, four hospitals operating in the same region are cooperating to cope with these shortages by developing a joint Talent Management Pool. ‘Coopetiton’ is a concept used for simultaneous cooperation and competition. In this paper, a case study is performed in order to enhance our understanding of coopetition. Among other things, the findings suggest that perceptions of organizational actors on competition differ and might hinder cooperative innovation with competitors, while perceived shared problems and resource constraints stimulate coopetition. We reflect on the current coopetition literature in light of the research findings, which have implications for future research on this topic.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Quality of Life in Italian Official Surveys

Quality-of-life studies have a 50-year history and inherited the tradition of the “social indicators” movement, born in the United States during the sixties and involving scholars and researchers, supported by the public administration and interested in gathering and analysing data aimed at studying non-economic components of societal wellbeing. The idea of quantifying “symptoms” (indicators) of living conditions has been launched by Italian statistician and criminologist, Alfredo Niceforo, who has been recognised as the pioneer of social-indicators concept. Moreover, with his book on Les indices numérique de la civilisation et du progrès, he may be considered the originator of an approach of comprehensive welfare and quality of life measurement as it is the concern of modern social indicators and quality of life.     

Validation of the Australian Midwifery Standards Assessment Tool (AMSAT): A tool to assess midwifery competence

Background

There is no current validated clinical assessment tool to measure the attainment of midwifery student competence in the midwifery practice setting. The lack of a valid assessment tool has led to a proliferation of tools and inconsistency in assessment of, and feedback on student learning.

The Social Bases of Rural‐Urban Political Divides: Social Status,Work, and Sociocultural Beliefs

The rural‐urban political divide has sparked media and social science concern. Yet national studies of rural and urban voters have largely failed to draw from the distinct conceptual literatures produced by rural sociologists. We take a new look at individuals’ voting choices, building from two rural sociological literatures, research on spatial inequality and on the rural‐urban continuum, to identify the social bases anteceding Republican voting in presidential elections. We analyze three social bases along which rural‐urban populations vary: social structural statuses, work and employment, and sociocultural values and beliefs. We question the degree to which rural‐urban differences can be accounted for by these factors. Data are from approximately 9,000 respondents to the General Social Surveys for election years 2000–2012. Our findings demonstrate that the literatures produced by rural sociologists provide a strong conceptual foundation for explaining rural‐urban voting differences. Rural and urban residents’ differential social statuses account for the greatest variation in their voting choices. Sociocultural values and beliefs, particularly attitudes toward domestic social issues, are also important. Findings add significant insight into the variety of factors that differentiate rural‐urban individuals’ voting choices as well as illuminate the need for greater emphasis on exurban voters.     

A Sociology of the Periphery Versus a Peripheral Sociology: Rural Sociology and the Dimension of Space1

Abstract Rural sociology is intrinsically concerned with the spatial dimensions of social life. However, this underlying research tradition, particularly the use of space as a research strategy, has been insufficiently addressed and its contributions to general sociology are little recognized. I outline how concern with space, uneven development, and the social relationships of peripheral settings have provided substantive boundary and conceptual meaning to rural sociology, propelled its evolution, and left it with a legacy of strengths, weaknesses, and challenges. A willingness to tackle the dimension of space and the thorny problems it raises often sets rural sociologists apart from other sociologists. This research tradition contrasted with general sociology's concern with developing generalization, aspatial covering laws, and proto-typical relationships of modern or Fordist development settings. Conceptual openings have left sociologists questioning their past agenda. Coupled with the “creative marginality” inherent in the questions and contexts addressed by rural sociologists, this makes the subfield central to contemporary sociology.