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.     

Women’s mobility and ‘transport-related social exclusion’ in Bishkek

This paper analyses gendered mobilities in Bishkek in the space of the most popular form of public transport: the minibus, or ‘marshrutka’. As the means by which women often access various important sites of daily life, the marshrutka itself is a site of negotiation and interaction. Utilizing theories of mobility and empirical data, we argue that marshrutkas are spaces that can give rise to two dichotomous conditions: positive marshrutka experiences may increase the social mobility of female passengers and subsequently increase social empowerment and influence, while negative ones can provide the grounds for social exclusion and gender inequality.     

Social work students' conception on roles within the family in Greece

The present article is based on a small-scale research that took place with third-year students in the department of social work at Technological Educational Institute of Patras. During class the students, who undertake the laboratory course ‘Social Work with Families’, were asked to discuss family roles and depict them on drawings. Analysing their drawings in a qualitative approach the findings suggest that students adopt traditional views on family issues and the family roles. Various gender stereotypes and prejudices were reflected in students' drawings and this is alarming for both social work education and practice.     

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.     

Feminized Forestry: The Promises and Pitfalls of Change in a Masculine Organization

The gendering of forestry as a distinctly masculine profession has led to a wide range of negative outcomes, including legal actions concerning discrimination, poor public perceptions and poor environmental records. Forestry organizations have addressed these concerns by attempting to increase the number of women in the profession. These efforts have been largely ineffective. Using the case of community‐based forestry, I argue that when we begin to consider not only women but also normatively feminine values as agents of change, our understanding of the profession of forestry may be rejuvenated.     

The value of being precise

This study investigates how individuals assess imprecise information. We focus on two essential dimensions of decision under uncertainty, outcomes and probabilities, and their respective precision. We believe the precision of information is highly relevant in the investment setting, as reflected in the well-known “home (familiarity) bias”, and the outcome and probability dimensions, separately or jointly, may affect investors’ knowledge of uncertainty and perceived risk of the investment options, and subsequently affect investors’ choices. To test this conjecture, we conducted three experiments. Our results show that 1) participants demonstrate a pattern of preference for precision and aversion of extreme vagueness and associate vagueness with higher perceived risk and lower investment (experiments one and two); 2) participants prefer vague outcome information to vague probability information (experiment two); 3) familiarity indeed positively affects the precision of estimated values, but this association is stronger for the outcome dimension than for probabilities (experiment three). Our results confirm that precision in information, especially in the outcome dimension has an impact on investors’ resource allocation choices.     

Having it all overseas: Aid workers and the international division of reproductive labour

This article examines the dynamics of power and privilege at work in international development through the prism of domestic service for expat aid workers in developing countries. Drawing on ethnographic fieldwork amid aid workers and their domestic staff in Dakar, Senegal, I argue that access to affordable care work greatly enhances the lives of women who work overseas in development. The postcolonial underdevelopment and poverty that aid work addresses is paradoxically critical to the aid workers' own access to affordable care, family balance and the means to do their jobs. I put this insight into the larger scholarly conversation about domestic work and global inequality, including on the Global Care Chain.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada