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.     

Cyclical Evolution of the Global Right

An understanding of the current right‐wing national and transnational social movements can benefit from comparing them to the global and national conditions operating during their last appearance in the first half of the twentieth century and by carefully comparing twentieth‐century fascism with the neofascist and right‐wing populist movements that have been emerging in the twenty‐first century. This allows us to assess the similarities and differences, and to gain insights about what could be the consequences of the reemergence of populist nationalism and fascist movements. Our study uses the comparative evolutionary world‐systems perspective to study the Global Right from 1800 to the present. We see fascism as a form of capitalism that emerges when the capitalist project is in crisis. World historical waves of right‐wing populism and fascism are caused by the cycles of globalization and deglobalization, the rise and fall of hegemonic core powers, long business cycles (the Kondratieff wave), and interactions with both Centrist Liberalism and the Global Left. We consider how crises of the global capitalist system have produced right‐wing backlashes in the past, and how a future terminal crisis of capitalism could lead to a reemergence of a new form of authoritarian global governance or a reorganized global democracy in the future.     

Acculturative stress in the adjustment of immigrant families

This study reviews the issue of acculturative stress in immigrant families. Acculturative stress includes behaviors experienced by immigrants that are a direct consequence of the process of acculturation and adaptation to a new society. A number of stressors impacting on the acculturation of immigrant families is discussed. These stressors include lack of English language skills, employment and economic status, educational background, family life, and sociopolitical and immigration status.     

Media Publicity and the Voluntary Sector: The Case of Nonprofit Organizations in New York City

Media publicity is an important resource for contemporary voluntary associations, but very little is actually known about the resources and organizational characteristics that are most important for getting media attention. To address this question, we collected and analyzed data on the organizational attributes and news publicity of 739 nonprofit organizations in New York City. We find that an organization's income, paid staff, membership size, and library resources are significantly related to getting media publicity, whereas the number of chapter affiliations is inversely related to publicity. Association type is also a significant factor that influences an organization's ability to get publicity. We discuss the implications that these findings have for current debates about advocacy and civic engagement in the nonprofit sector.     

Lives in limbo: Temporary Protected Status and immigrant identities

The United States formulates much of its immigration and refugee policy to match economic and political circumstances. We interpret these policy shifts as a set of graduated positions on immigration and refugee flows that attempts to discipline the lives of newcomers and, in so doing, shapes immigrant identities. In this article, we analyse the interplay between the US government and Salvadoran asylum applicants negotiating procedures that grant only temporary relief from deportation via the policy of Temporary Protected Status (TPS). We find that each policy shift results in the strategic renegotiation of asylum applicants’ identities so as to achieve the best opportunity for a successful outcome. Based on Foucault’s ideas of governmentality and Ong’s concept of flexible citizenship, we argue that what appears more superficially as a patchwork strategy of immigration laws and asylum practices may be theorized more deeply as a set of flexible responses by the state that turn on identity construction at different scales, and that aim to mediate transnational relations.     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.