Égalité entre hommes et femmes et gouvernance des chaînes d'approvisionnement mondiales: promouvoir les droits des travailleuses

La gouvernance privée telle qu'elle découle des différentes initiatives prises par les multinationales pour promouvoir le respect de normes sociales et l'égalité hommes‐femmes a eu peu d'effet sur les discriminations fondées sur le genre dans les chaînes d'approvisionnement mondiales. Les auteures soulignent l'intérêt des Principes directeurs des Nations Unies relatifs aux entreprises et aux droits de l'homme en la matière, estimant qu'il est indispensable de recourir à une gouvernance «stratifiée» associant public, privé et acteurs sociaux pour lutter contre cette discrimination et intégrer le souci de l'égalité des sexes dans l'exercice de la diligence raisonnable exigée en matière de droits de l'homme.     

Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

This is a comparative study of various clustering and classification algorithms as applied to differentiate cancer and non-cancer protein samples using mass spectrometry data. Our study demonstrates the usefulness of a feature selection step prior to applying a machine learning tool. A natural and common choice of a feature selection tool is the collection of marginal p-values obtained from t-tests for testing the intensity differences at each m/z ratio in the cancer versus non-cancer samples. We study the effect of selecting a cutoff in terms of the overall Type 1 error rate control on the performance of the clustering and classification algorithms using the significant features. For the classification problem, we also considered m/z selection using the importance measures computed by the Random Forest algorithm of Breiman. Using a data set of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration and National Cancer Institute Clinical Proteomics Database, we undertake a comparative study of the net effect of the machine learning algorithm–feature selection tool–cutoff criteria combination on the performance as measured by an appropriate error rate measure.     

Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion

AStA Advances in Statistical Analysis - Count data as response variables are commonly modeled using Poisson regression models, which require equidispersion, i.e., equal mean and variance. However,...     

Newborns' Perceptual Categorization for Closed and Open Geometric Forms

This study explored newborns' ability to perceive perceptual similarities between different exemplars of 2 broad classes of simple shapes: closed and open geometric forms. Three experiments were carried out using a visual paired‐comparison task. Evidence showed that, after familiarization either to closed‐shaped or to open‐shaped forms, newborns manifested a novelty preference for a novel‐category rather than for a familiar‐category exemplar (Experiment 1). This result could not be explained either as a consequence of the newborns' inability to discriminate between instances of the same category of simple geometric forms (Experiment 2), or as a consequence of a spontaneous preference for the novel‐category exemplars (Experiment 3). Overall, findings revealed that newborns are able to form broad categories of distinguishable geometric shapes by relying on the shapes' perceptual similarity.     

Parametric estimation for ARFIMA models via spectral methods

Given a fractional integrated, autoregressive, moving average,ARFIMA (p, d, q) process, the simultaneous estimation of the short and long memory parameters can be achieved by maximum likelihood estimators. In this paper, following a two-step algorithm, the coefficients are estimated combining the maximum likelihood estimators with the general orthogonal decomposition of stochastic processes. In particular, the principal component analysis of stochastic processes is exploited to estimate the short memory parameters, which are plugged into the maximum likelihood function to obtain the fractional differencingd.     

Yearning, learning, and conceding: reasons men and women change their childbearing intentions

People's childbearing intentions change over the course of their reproductive lives. These changes have been conceptualized as occurring in response to the realization that an individual is unlikely to achieve his or her intended fertility, because of constraints such as the "biological clock" or lack of a partner. In this article, we find that changes to child-bearing plans are influenced by a much wider range of factors than this. People change their plans in response to the wishes of their partners, in response to social norms, as the result of repartnering, and as the result of learning about the costs and benefits of parenthood; there are also differences between the factors that influence men's and women's decision-making. In a departure from existing studies in this area, we use a flexible analytical framework that enables us to analyze increases in planned fertility separately from decreases. This allows us to uncover several complexities of the decision-making process that would otherwise be hidden, and leads us to conclude that the determinants of increases in planned fertility are not simply equal and opposite to the determinants of decreases.