Consecuencias de la sobrecarga del Estado y la globalización en la concepción del Estado de bienestar. Hacia un régimen de prestaciones sociales condicionadas

El Estado de bienestar necesita el replanteamiento de las bases en las que se asienta, tanto por causa de la crisis de sobrecarga como por causa de la globalización. Estas razones han ayudado a las perspectivas que abogan por un modelo de Estado de bienestar alejado del keynesiano, que hace al ciudadano más responsable por la procura de su propio bienestar en un mundo regido por la competitividad económica. Valgan como ejemplo los modelos de Estado teorizados por Bob Jessop («Estado schumpeteriano de workfare»), Philip Cerny («Estado de competición»), Philip Bobbit («Estado de mercado») o Anthony Giddens («Estado social inversor»), en los cuales se somete la distribución de bienestar a las exigencias del mercado.Welfare State needs the rethinking of the bases in which is settled due to its overload crisis as well as due to globalization. Those reasons have brought the arrival of new State concepts far from keynesian model, which make the citizens more responsible for their own wellbeing in a world ruled by economic competition. Models theorized by Bob Jessop («Schumpeterian Workfare State»), Philip Cerny («Competition State»), Philip Bobbit («Market State») or Anthony Giddens («Investor Social State»), could be seen as an example of how the welfare is submitted to market

L’aplication de la Methode Indicametrique a l’Analyse des Projets et Programmes: Cas du Secteur Emploi-Formation en Cote d’Ivoire

Le présent article s’inscrit dans le cadre de notre travail de recherche de thèse de doctorat en gestion de projets portant sur le thème : « Structure du marché du travail en Côte d’Ivoire : une analyse descriptive à la lumière des projets et programmes d’emploi ». A travers cette thématique, vous voulons contribuer à l’amélioration de l’employabilité et de l’insertion des personnes en âges de travailler, notamment les jeunes diplômés. Il s’agit également de promouvoir de nouveaux paradigmes et outils en matière de conception et d’analyse de risques de projets, en plus de ceux déjà existants. En effet, les faibles taux de chômage observés au cours des enquêtes emploi de ces cinq dernières années ( enquêtes emploi 2012, 2014, 2016, 2017 et 2019) voilent une résurgence des emplois informels et précaires au détriment des emplois formels. La population au chômage est majoritairement composée de primo demandeurs d’emploi, notamment les diplômés de l’enseignement technique, professionnel ainsi que de l’enseignement supérieur. Ce qui pose d’ailleurs, la question de l’efficacité externe du système national de formation, ainsi que des projets et programmes d’insertion. Pour ce faire, nous avons jugé judicieux de passer en revue une série de treize ( 13) projets mis en œuvre ou en cours de réalisation, en nous servant des outils de la science indicamétrique. Cette discipline a la particularité de mettre en exergue les facteurs susceptibles d’engendrer la performance, l’efficacité et l’efficience d’un projet, en se basant sur les capacités intrinsèques des personnes physiques et morales impliquées dans sa gestion. Au plan méthodologique, nous avons recours à un des outils importants de la science en indicamétrie : la carte capacitaire. Elle comprend un ensemble d’indicateurs qui s’expriment en Valeur Intrinsèque Capacitaire Energétique (VICE) normée. Toute valeur en dessous de la norme est considérée insuffisante ou en déficit. Toute valeur au-dessus de la norme est dite, en excédent. Ainsi, à l’issue des différentes analyses, nous avons abouti aux principaux résultats suivants : (i) la prise en compte des caractéristiques de l’environnement interne et externe des projets accroit leur probabilité de succès, (ii) le choix de la dénomination de tout projet ou programme influence son mode de gestion et (iii) la prise en compte des caractéristiques et des capacités intrinsèques des promoteurs ou gestionnaires des projets dans leur mise en œuvre accroit significativement leur probabilité de succès.   This paper is part of the research work for a doctoral thesis in project management titled "Structure of the labor market in Côte d'Ivoire: a descriptive analysis in the light of projects and programs of use”. The objective of this theme is to improve the employability and integration of people of working age, especially young graduates. It also focuses on promoting new paradigms and tools for project design and risk analysis, in addition to existing ones. The low unemployment rates observed during employment surveys over the past five years veil a resurgence of informal and precarious jobs to the detriment of formal jobs. The unemployed population is mainly made up of first-time job seekers, particularly graduates of technical, vocational, and higher education. This raises the question of the external effectiveness of the national training system as well as the integration projects and programs. To answer this question, a series of thirteen (13) projects that were either implemented or in progress was reviewed through the tools of indicametric science. This discipline has the particularity of highlighting the factors that are likely to generate the performance, effectiveness, and efficiency of a project based on the intrinsic capacities of the natural and legal persons involved in its management. As for the methodology, there is recourse to one of the important indicametry tools: the capacity card. It includes a set of indicators which are expressed in standardized Intrinsic Energy Capacitance Value (VICE). Any value below the standard is considered insufficient or in deficit, while any value above the norm is said to be in excess. At the end of the various analysis, the following main results have been achieved: (i) taking into account the characteristics of the internal and external environment of projects increases their probability of success, (ii) the choice of name of any project or program influences its mode of management and (iii) taking into account the characteristics and intrinsic capacities of the promoters or managers of projects in their implementation significantly increases their probability of success

Plasticity evolution of an aluminum-magnesium alloy under abrupt strain path changes

During the forming and manufacturing of engineering materials, plasticity behavior could be evolving significantly due to complex deformation history. Therefore, this study aims to characterize the plasticity evolution of an aluminum-magnesium alloy under simple monotonic and non-monotonic loading with abrupt strain path changes. Instead of focusing only on one single stress state in the first-step loading for most of the studies in the literature, the current non-monotonic strain path testing program investigates three stress states – uniaxial, plane-strain, and biaxial tension – in the first-step loading and combines them with a second-step uniaxial loading along and orthogonal to the initial loading direction. This combination generates non-monotonic stress–strain data in a quite large and distributed spectrum in terms of the Schmitt parameter. It is found that the aluminum-magnesium alloy shows a unique phenomenon with a lower yield strength at reloading compared to monotonic cases coupled with a steady increase of stress overshooting the monotonic one at large strains. This increase of stress as well as the strain hardening rate lasts till the uniform strain and is therefore referred to as permanent hardening. The comprehensive non-monotonic behavior delivered by the new experimental program in this study could further assist the development of material models and an in-depth understanding of the underlying mechanisms

Efficient approximation of probability distributions with k-order decomposable models

During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose the fractal tree family of algorithms which approximates this problem with a computational complexity of O(k 2 · n 2 · N ) in the worst case, where n is the number of implied random variables and N is the size of the training set. The fractal tree algorithms construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy that decomposes the problem into a set of separator problems. Each separator problem is efficiently solved using the generalized Chow-Liu algorithm. Fractal trees can be considered a natural extension of the Chow-Liu algorithm, from k = 2 to arbitrary values of k, and they have shown a competitive behaviour to deal with the maximum likelihood problem. Due to their competitive behavior, their low computational complexity and their modularity, which allow them to implement different parallelization strategies, the proposed procedures are especially advisable for modelling high dimensional domains.Saiotek and IT609-13 programs (Basque Government) TIN2013-41272-P (Spanish Ministry of Science and Innovation) COMBIOMED network in computational bio-medicine (Carlos III Health Institute

Learning a logistic regression with the help of unknown features at prediction stage

The use of features available at training time, but not at prediction time, as additional information for training models is known as learning using privileged information paradigm. In this paper, the handling of privileged features is addressed from the logistic regression perspective, commonly used in the clinical setting. Two new proposals, LOGIT+ and LRPROB+, learned with the influence of privileged features and preserving the interpretability of conventional logistic regression, are proposed. Experimental results on datasets report improvements of our proposals over the performance of traditional logistic regression learned without privileged information

Efficient approximation of probability distributions with k-order decomposable models

During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for $k<2$. In this work, we propose the fractal tree family of algorithms which approximates this problem with a computational complexity of $\mathcal{O}(k \cdot n^2 \log n)$ in the worst case, where $n$ is the number of implied random variables and N is the size of the training set. The fractal tree algorithms construct a sequence of maximal $i$-order decomposable graphs, for $i=2,...,k,$ in $k - 1$ steps. At each step, the algorithms follow a divide-and-conquer strategy that decomposes the problem into a set of separate problems. Each separate problem is efficiently solved using the generalized Chow-Liu algorithm. Fractal trees can be considered a natural extension of the Chow-Liu algorithm, from $k = 2$ to arbitrary values of $k$, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their competitive behavior, their low computational complexity and their modularity, which allow them to implement different parallelization strategies, the proposed procedures are especially advisable for modeling high dimensional domains

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out

SELF-DISCLOSURE KOMUNITAS STOIC INDONESIA DI MEDIA SOSIAL FACEBOOK

Abstract. Stoic Indonesia is a community on Facebook where the members can do a self-disclosure. The members' self-disclosure is related to the popularity of Stoic philosophy that can help people to control negative emotions. This study examines the form of members' self-disclosure in the Indonesian Stoic Community through Facebook. The research method used in this study is descriptive qualitative method using the Johari Window Theory. The data collected with in in-depth interview techniques, observation, and documentation. Then analyzed with data reduction, data analysis, and data verification. The results of this study indicate that the members' self-disclosure Stoic Indonesia Facebook group occurs because of a sense of trust, safety and comfort for other members even though some members use anonymous accounts. The Stoic Indonesia Facebook group also helps its members to find solutions to various problems so that members get rewards or benefits when doing self-disclosure. Keywords: Self-disclosure, Stoic Indonesia, Facebook, Johari Window Abstrak. Stoic Indonesia merupakan komunitas di media sosial Facebook yang bisa menjadi wadah anggotanya untuk melakukan self-disclosure. Self-disclosure para member berkaitan dengan kepopuleran aliran filsafat Stoikisme yang bisa membantu mengontrol emosi negatif dalam diri seseorang. Penelitian ini mengkaji bagaimana bentuk pengungkapan diri anggota di Komunitas Stoic Indonesia melalui media sosial Facebook. Metode penelitian yang digunakan adalah metode kualitatif deskriptif dengan menggunakan Teori Johari Window. Pengumpulan data dalam penelitian ini menggunakan teknik wawancara mendalam, observasi, dan dokumentasi dengan teknik analisis data menggunakan reduksi data, analisis data, dan verifikasi data. Hasil penelitian ini menunjukkan self-disclosure atau bentuk pengungkapan diri anggota di grup Facebook Stoic Indonesia terjadi karena adanya rasa kepercayaan, aman dan nyaman kepada anggota lain meski beberapa anggota menggunakan akun anonim. Grup Facebook Stoic Indonesia juga membantu para anggotanya untuk menemukan solusi atas berbagai permasalahan sehingga anggota mendapat imbalan atau manfaat saat melakukan self-disclosure. Kata kunci: Self-disclosure, Stoic Indonesia, Facebook, Johari Windo