Whistleblowers: an essential resource for the sustainable prevention of risks in sociotechnical systems

Our world of industry and technology has, over the years, has seen undeniable successes in terms of safety and reliability. But major catastrophes and dramatic accidents continue, even today, to cause major human and material losses and to threaten the environment with pollution on a massive scale. Could these disasters and these accidents have been foreseen and avoided? Would it have been possible to anticipate their occurrence by detecting signals of potential hazards? It is unsettling to notice, through retrospective analysis of such events, that warnings had been issued long before the catastrophe or accident took place. This raises several questions, which we will attempt to address in this paper. Why are whistleblowers often not listened to, threatened, or simply ignored? Why are their warnings viewed as "bad omens" instead of essential resources to ensure safety? Do whistleblowers stand idly by, or do they implement individual and collective strategies to make themselves heard? Which managerial and organizational conditions are conducive to developing empowerment in whistleblowers? Based on four case studies, we attempt to address these questions, and offer a first level of analysis and explanation by proposing and defining two new concepts: operative resilience and strategic resilience.     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Législation sur la protection de l'emploi et stabilité des emplois: une analyse transversale en Europe

S'appuyant sur les données d'Eurostat pour la période 1999–2006, les auteurs étudient l'évolution de l'ancienneté dans l'emploi des salariés de l'Union européenne. Ils n'observent pas de baisse généralisée de cette ancienneté, mais un raccourcissement tendanciel parmi les jeunes travailleurs (15–24 ans) dans de nombreux pays. Leurs résultats montrent que cette tendance est associée à une plus faible protection de l'emploi conférée et par le droit et par la présence de syndicats. Etant donné le faible pouvoir de négociation individuelle des jeunes travailleurs, la tendance à une plus forte individualisation des relations d'emploi pourrait, en effet, plus particulièrement les toucher.     

Reshaping the Social: A Comparison of Fasu and Kubo-Febi Approaches to Incorporating Land Groups

In Papua New Guinea, local communities increasingly strive to render themselves ‘visible’ to the state as entities entitled to control and claim benefits from developments on their land. When people commit expressions of social form to paper they inevitably reshape previously operative social and political forms. This article compares ways in which two groups of Papua New Guineans have gone about the process of forming Incorporated Land Groups. Different histories of resource extraction, associated differences in engagement with state and companies and different pre-existing social forms are all reflected in the negotiations described. We discuss people's motivations for drawing up lists of members, the ways they went about this and implications for the communities themselves and for ILG registration more generally. Our analysis draws attention to how outcomes are shaped by scales of differentiation and emergent inequalities within the legal entities being imagined.     

Smooth Conditional Distribution Function and Quantiles under Random Censorship

We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional -quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994).