Flexicurity in Belgium: A reform proposal based on economic principles

Belgium's systems of unemployment insurance and employment protection legislation were set up in the context of typically enduring and stable relationships between workers and firms. Economic globalization and rapid technological and organizational change, however, are now demanding more flexibility of both workers and firms. As a result, current labour market institutions are in urgent need of reform to reconcile such increased flexibility with adequate security for workers. Though the call for a “flexicurity” approach is not new, there is no single view of what institutional model the latter implies. This paper proposes a reform explicitly guided by economic principles.     

Urban and agricultural soils: conflicts and trade-offs in the optimization of ecosystem services

On-going human population growth and changing patterns of resource consumption are increasing global demand for ecosystem services, many of which are provided by soils. Some of these ecosystem services are linearly related to the surface area of pervious soil, whereas others show non-linear relationships, making ecosystem service optimization a complex task. As limited land availability creates conflicting demands among various types of land use, a central challenge is how to weigh these conflicting interests and how to achieve the best solutions possible from a perspective of sustainable societal development. These conflicting interests become most apparent in soils that are the most heavily used by humans for specific purposes: urban soils used for green spaces, housing, and other infrastructure and agricultural soils for producing food, fibres and biofuels. We argue that, despite their seemingly divergent uses of land, agricultural and urban soils share common features with regards to interactions between ecosystem services, and that the trade-offs associated with decision-making, while scale- and context-dependent, can be surprisingly similar between the two systems. We propose that the trade-offs within land use types and their soil-related ecosystems services are often disproportional, and quantifying these will enable ecologists and soil scientists to help policy makers optimizing management decisions when confronted with demands for multiple services under limited land availability.     

The Examination of Emotional Facial Expressions Within Parent–Child and Sibling Interactive Contexts: A Systematic Review

This work constitutes a systematic review of the empirical literature about emotional facial expressions displayed in the context of family interaction. Searches of electronic databases from January 1990 until December 2016 generated close to 4400 articles, of which only 26 met the inclusion criteria. Evidence indicate that affective expressions were mostly examined through laboratory and naturalistic observations, within a wide range of interactive contexts in which mother–child dyads significantly outnumbered father–child dyads. Moreover, dyadic partners were found to match each others’ displays and positive and neutral facial expressions proving more frequent than negative facial expressions. Finally, researchers observed some developmental and gender differences regarding the frequency, intensity, and category of emotional displays and identified certain links among facial expression behavior, family relations, personal adjustment, and peer-related social competence.     

Alexithymia and Gambling: A Risk Factor for All Gamblers?

The purpose of this study was to investigate alexithymia (in relation with depression) in three groups of French gamblers (n = 186) recruited in their gambling location: at the racetracks (n = 80 males; mean age 28.1 years), in the slot machine rooms (n = 65; 29 males, 36 females; mean age 34.6 years), and in the traditional gaming rooms (n = 41 males; mean age 36 years). Gambling behavior was measured by the South Oaks Gambling Screen and DSM-IV criteria for pathological gambling, Alexithymia by the Toronto Alexithymia Scale (TAS-20) and depression with the Beck Depression Inventory (BDI-13). For racetracks and slot machine gambling, pathological gamblers differed from non-pathological gamblers, regarding their alexithymia scores. These results remained stable after controlling for depression scores among the racetracks gamblers only. The relationship between alexithymia and depression depends on the type of pathological gambler. These findings are consistent with the idea of identifying clinically distinct subgroups of gamblers.     

Lying in defence of privacy: anthropological and methodological observations

This article deals with three topics: lying, privacy and anthropological research. Their complex intertwinement is analysed using fieldwork notes and through engagement with relevant literature from various disciplines. Experiences of privacy, among researchers as well as among respondents, is underexposed in the literature on social research methodology. Furthermore, lying is sometimes the only effective way to protect one’s privacy. Starting from a research experience with lying respondents in Ghana, I discuss the various circumstances and reasons that lead to lying in defence of privacy, and in particular, concerns about respect in the context of research. Next, I return to the concept of privacy and explicate cultural variations in the experience of privacy. I then look at the consequences for research ethics. The paradoxical conclusion is that lying, a discredited tool of deception, is often applied in order to uphold a widely accepted basic human necessity of life – privacy. Qualitative social researchers should take these concerns about privacy and respect into account and engage in conversations that do not threaten the security of their interlocutors.     

Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response

Randomized response is a misclassification design to estimate the prevalence of sensitive behaviour. Respondents who do not follow the instructions of the design are considered to be cheating. A mixture model is proposed to estimate the prevalence of sensitive behaviour and cheating in the case of a dual sampling scheme with direct questioning and randomized response. The mixing weight is the probability of cheating, where cheating is modelled separately for direct questioning and randomized response. For Bayesian inference, Markov chain Monte Carlo sampling is applied to sample parameter values from the posterior. The model makes it possible to analyse dual sample scheme data in a unified way and to assess cheating for direct questions as well as for randomized response questions. The research is illustrated with randomized response data concerning violations of regulations for social benefit.     

Smooth estimation of a monotone density

We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence of [Formula: See Text] at a fixed point t if the density has a negative derivative at t. The same rate is obtained by a kernel estimator of bandwidth [Formula: See Text], but the limit distributions are different. If the density is both differentiable at t and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence [Formula: See Text], and compare the limit distributions of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behaviour of a kernel estimator with a bandwidth larger than [Formula: See Text], in the case that the density is known to have more than one derivative.     

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].     

Identity for the NPMLE in Censored Data Models

We derive an identity for nonparametric maximum likelihood estimators (NPMLE) and regularized MLEs in censored data models which expresses the standardized maximum likelihood estimator in terms of the standardized empirical process. This identity provides an effective starting point in proving both consistency and efficiency of NPMLE and regularized MLE. The identity and corresponding method for proving efficiency is illustrated for the NPMLE in the univariate right-censored data model, the regularized MLE in the current status data model and for an implicit NPMLE based on a mixture of right-censored and current status data. Furthermore, a general algorithm for estimation of the limiting variance of the NPMLE is provided. This revised version was published online in July 2006 with corrections to the Cover Date.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.