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.     

When Working Hard Is Not Enough for Female and Racial/Ethnic Minority Apprentices in the Highway Trades

Drawing on employment records, qualitative interviews, and a survey, we explore the experiences of apprentices in the highway trades in Oregon. We demonstrate that female and racial/ethnic minority apprentices have lower rates of recruitment and retention and disproportionately face challenges with interpersonal interactions, hiring practices, and supervisory practices. Yet, we find a pervasive narrative that attributes apprentices' success to “hard work,” which contributes to the legitimacy of these inequalities. Consistent with the conceptualization of work organizations as inequality regimes, we argue that the apprenticeship system has policies, practices, and ideologies that are on the surface gender and race/ethnicity neutral, yet lead to the perpetuation of inequalities.     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

Standing on Their Own Two Feet: How the New Public Charge Rules Could Impact Non-European LPR Applicants

Population Research and Policy Review - In February 2020, the U.S. government began to implement a new Public Charge rule that greatly expands the definition of “public charge” when...     

Liminality in Management and Organization Studies: Process,Position and Place

This paper explores liminality, a concept receiving increased attention in management and organization studies and gaining prominence because of its capacity to capture the interstitial and temporary elements of organizing and work. The authors present a systematic review of the literature on liminality, covering 61 published papers, and undertake a critical analysis of how the concept of liminality has been used in prior research. This review reveals associations with three main themes: process; position; and place. For each theme, the authors identify the central research questions posed, while comparing individual and collective levels of analysis. During this process, the authors revisit several ideas central to the original, anthropological research on liminality, a perspective from which they suggest a rejuvenation of liminality research in management and organization studies. This paper argues for a greater focus on the liminal experience itself – especially its ritual and temporal dimensions – and for improving the comparative analysis of liminality following the three themes identified in this paper. The authors suggest that revising the agenda for liminality research along these lines could facilitate more informed responses to the challenges of an increasingly temporary and dynamic work life.     

The Welfare State as Safety Net in Migration Preferences: Empirical Evidence from an Experiment Among Dutch Master Students

Population Research and Policy Review - The welfare state can be perceived as a safety net which helps individuals adjust to situations of risk or transition. Starting from this idea of the welfare...     

Can Children Recognize Bodily Expressions of Emotion?

Journal of Nonverbal Behavior - Past research has demonstrated that children understand distinct emotion concepts and can accurately recognize facial expressions of distinct emotions by a young...     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.