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.     

Negotiating the Intersection of Racial Oppression and Heteronormativity

This qualitative study examines the narratives of seven heterosexual, cisgender individuals who identify both as persons of color and as positively disposed toward LGB and transgender-identified persons. Using psycho-discursive qualitative methodology, the authors will present the narrative strategies taken up by these positively disposed cisgender heterosexuals of color as they attempt to position themselves as supportive of LGB and transgender persons while negotiating the discourse of heteronormativity. The three narrative strategies have been titled differentiation, empathy, and coherence. In addition to mapping the three narrative strategies, the authors also explore why informants may choose certain strategies over others and argue that the intersection of social identities must be considered when attempting to understand social oppression. Implications for the human services fields will be discussed.     

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.     

Punishment and pleasure: women, food and the imprisoned body1

Food assumes enormous importance in prison: for many prisoners it conditions their life in custody and, in many respects, is symbolic of the prison experience. This article explores the complex relationship between gender, food and imprisonment through an analysis of data obtained from in‐depth interviews and group discussions conducted in three women's prisons in England. The findings indicate that, in prison, where control is taken away as the prisoner and her body become the objects of external forces, food is experienced not only as part of the disciplinary machinery, but also as a powerful source of pleasure, resistance and rebellion. The implications of such findings for health promotion in the prison context are discussed. Here, the pleasures and consolations of food may well constitute a redefinition of what it is to be healthy in this context, one that challenges the dominant meaning constructed in current health promotional discourse.     

The Abject Embodiment of Cancer Patients: Dignity,Selfhood, and the Grotesque Body

The body is the empirical quintessence of the self. Because selfhood is symbolic, embodiment represents the personification and materialization of otherwise invisible qualities of personhood. The body and experiences of embodiment are central to our sense of being, who we think we are, and what others attribute to us. What happens, then, when one's body is humiliating? How does the self handle the implications of a gruesome body? How do people manage selfhood in light of grotesque physical appearances? This study explores these questions in the experiences of dying cancer patients and seeks to better understand relationships among body, self, and situated social interaction.     

Consent and Informed Consent: Their Ongoing Evolutions in Clinical Care and Research on Humans

The dual concepts of 'consent' and 'informed consent' continue to have three evolutions. The primary evolution of consent in the patient–physician relationship began in Great Britain in 1767 in the British case, Slater v. Baker and Stapleton , in the judge-made law of consent. The term 'informed consent' within the patient–physician relationship entered the judicial lexicon in the 1957 California appellate case, Salgo v. Leland Stanford Junior University . In its second evolution within research on humans following the Nuremberg trials that included experimental atrocities on humans, there is a key focus on clarifying the purpose of research and specifying the reasons for the need for an even more extensive risk disclosure to individuals considering volunteering for study participation. This second evolution continues with the further refinement of the Declaration of Helsinki and, within the USA, a focus on the Belmont Report . In its third evolution in research in the social science, there has been a recognition of problems with informed consent to questionnaire research. When questionnaires involve patients with moderate or severe posttraumatic stress disorder or abused individuals, there needs to be intense consideration focused on how to best protect the participants with these conditions during the questionnaire study.