Managing Risk as a Duality of Harm and Benefit: A Study of Organizational Risk Objects in the Global Insurance Industry

This study examines how organizations construct and manage risk objects as a duality of harm–benefit within their normal operations. It moves beyond the existing focus on accidents, disasters and crisis. We study the risk‐transfer processes of 35 insurers where they navigate the tension of retaining risk in their insurance portfolio to increase the benefit of making profit and transferring risk to reinsurance to reduce the harm of paying claims. We show that organizations’ constructions of risk are underpinned by everyday risk management practices of centralizing, calculating and diversifying. Through variation in these practices, not all organizations seek balance and we in turn uncover the sensemaking processes of abstracting and localizing that enable organizations to prioritize harm or benefit. This contributes to the risk literature by illuminating the co‐constitutive relationship between risk sensemaking processes and everyday risk management practices. Following the complex linkages involved in the construction of risk objects as sources of harm–benefit, our analysis also contributes to the literature on dualities. It shows that while immediate trade‐offs between harm–benefit occur, prioritizing one element of the duality is ultimately a means for attaining the other. Thus, while initial imbalance is evident, prioritization can be an enabling approach to navigating duality.     

Scheduling Updates of Probabilistic Risk Assessments: The Arkansas Nuclear One-Unit 1 Experience

This paper presents the results of a study that identified how often a probabilistic risk assessment (PRA)should be updated to accommodate the changes that take place at nuclear power plants. Based on a 7-year analysis of design and procedural changes at one plant, we consider 5 years to be the maximum interval for updating PRAs. This conclusion is preliminary because it is based on the review of changes that occurred at a single plant, and it addresses only PRAs that involve a Level 1 analysis (i.e., a PRA including calculation of core damage frequency only). Nevertheless, this conclusion indicates that maintaining a useful PRA requires periodic updating efforts. However, the need for this periodic update stems only partly from the number of changes that can be expected to take place at nuclear power plants–changes that individually have only a moderate to minor impact on the PRA, but whose combined impact is substantial and necessitates a PRA update. Additionally, a comparison of two generations of PRAs performed about 5 years apart indicates that PRAs must be periodically updated to reflect the evolution of PRA methods. The most desirable updating interval depends on these two technical considerations as well as the cost of updating the PRA. (Cost considerations, however, were beyond the scope of this study.)     

MEANINGFUL REGRESSION AND ASSOCIATION MODELS FOR CLUSTERED ORDINAL DATA

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, the association structure is often of equal interest—for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness, and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles.     

L'Intervention Sociologique After Twenty-Five Years: Can It Translate into English?

The sociological intervention is a research method developed in France by Alain Touraine in 1976 to explore social movement action. Since then it has undergone significant development, both in terms of the type of social experience explored and the broader research program framing it. Despite its importance, the intervention remains little known and virtually untested within English-language sociology. This article examines key dimensions of this research method through discussing several examples, and argues that the intervention has much to offer English-language sociologists concerned with questions of agency and reflexivity, relationship, narrative and experience.     

A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.     

A sea change on the island continent: frameworks for risk assessment,prevention and intervention in child health in Australia

This article overviews prevention and early intervention approaches focusing specifically on their relevance to the health of children and young people in Australia. Australian public health has a sound track record although concealed within the aggregate profile are a number of sub-populations with poorer health indicators. Recognition of this has increased efforts to improve the health of children and young people especially in exploring the impact of social environments within the communities where children are raised. This paper examines emerging research in this area drawing out key lessons and learning from Australian experience in the field of early intervention and prevention in community settings.     

Clinical supervision: Its role in “Containing” countertransference responses to a filicidal patient

This paper illustrates the role of supervision in processing countertransference responses with might have disrupted the therapist's empathy with a patient who had killed her child. The clinical material demonstrates how the therapist's responses of initial denial and subsequent disgust and fear were dealt with in supervision. As a result, the treatment process led to genuine, albeit limited, therapeutic change in a patient with severe character pathology.This paper is developed from a presentation of the case of Mrs. Jay at Department of Psychiatry Grand Rounds, University of Cincinnati College of Medicine, on May 2, 1989.     

Estimation and diagnostic for skew-normal partially linear models

Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.