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The politics of Josephine Baker's adopted family cut cross the conventional divides between radical and conservative, transnational and national, and European and American. In studying her family, then, we can come to better understand the practices of diaspora and transnationalism in the twentieth century. We can more brightly illuminate the process of circulation, mediation, and translation that sat at the heart of Baker's assemblage. And we can provide a more sharply focused analysis of Baker's distinctive, celebrity-inflected feminism.

Article disponible en ligne sur : http://cultureandhistory.revistas.csic.es/index.php/cultureandhistory/article/view/110/376 ; International audience ; Based on a close and detailed investigation of local and strangely neglected municipal sources, combined with the meticulous scrutiny of documents conserved in the Russian archives for the period 1559-1562, and a focus on institutional history, I demonstrate how the early Calvinistic consistories cleverly manipulated the particular municipal organization (the consulates) of Midi communities and managed to take them over with relative ease. In many of these communities, which greatly varied in size, we find that the consistories were turned into "political councils"; this subsequently enabled them to control the election of magistrates (consuls) and, even before the beginning of the wars of Religion, to ensure that they controlled the municipalities, though the Protestants were very much a minority. This is a major factor towards explaining the famous "Protestant crescent" that characterizes the South of France with its tones of civil religion.

Article disponible en ligne sur : http://cultureandhistory.revistas.csic.es/index.php/cultureandhistory/article/view/110/376 ; International audience ; Based on a close and detailed investigation of local and strangely neglected municipal sources, combined with the meticulous scrutiny of documents conserved in the Russian archives for the period 1559-1562, and a focus on institutional history, I demonstrate how the early Calvinistic consistories cleverly manipulated the particular municipal organization (the consulates) of Midi communities and managed to take them over with relative ease. In many of these communities, which greatly varied in size, we find that the consistories were turned into "political councils"; this subsequently enabled them to control the election of magistrates (consuls) and, even before the beginning of the wars of Religion, to ensure that they controlled the municipalities, though the Protestants were very much a minority. This is a major factor towards explaining the famous "Protestant crescent" that characterizes the South of France with its tones of civil religion.

AbstractWinemakers in South France combine contingently institutional logics to achieve economic performance. Entrepreneurs who decide to cooperate with their competitors must manage four phases of "coopetition": launch, formalization, protection, and reinforcement. They engage in the coopetition process by referring to a professional logic that becomes dominant, and they complete the process by fostering a community logic that is combined with the professional logic. Identity and legitimacy are the key elements that entrepreneurs segregate and blend, which entails new combinations in the respective influential power of state logic, professional logic and community logic.

International audience ; Nowadays, air pollution is a major treat for public health, with clear links with many diseases, especially cardiovascular ones. The spatio-temporal study of pollution is of great interest for governments and local authorities when deciding for public alerts or new city policies against pollution raise. The aim of this work is to study spatio-temporal profiles of environmental data collected in the south of France (Région Sud) by the public agency AtmoSud. The idea is to better understand the exposition to pollutants of inhabitants on a large territory with important differences in term of geography and urbanism. The data gather the recording of daily measurements of five environmental variables, namely three pollutants (PM10, NO2, O3) and two meteorological factors (pressure and temperature) over six years. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing need of methods to summarize and understand them. For this purpose, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each co-cluster a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler, is proposed for model inference, as well as a model selection criteria for choosing the number of co-clusters. The application of the proposed co-clustering algorithm on environmental data of the Région Sud allowed to divide the region composed by 357 zones in six macro-areas with common exposure to pollution. We showed that pollution profiles vary accordingly to the seasons and the patterns are conserved during the 6 years studied. These results can be used by local authorities to develop specific programs to reduce pollution at the macro-area level and to identify specific periods of the year with high pollution peaks in order to set up specific prevention programs for health. Overall, the proposed co-clustering approach is a powerful resource to analyse multivariate functional data in order to identify intrinsic data structure and summarize variables profiles over long periods of time.

International audience ; Nowadays, air pollution is a major treat for public health, with clear links with many diseases, especially cardiovascular ones. The spatio-temporal study of pollution is of great interest for governments and local authorities when deciding for public alerts or new city policies against pollution raise. The aim of this work is to study spatio-temporal profiles of environmental data collected in the south of France (Région Sud) by the public agency AtmoSud. The idea is to better understand the exposition to pollutants of inhabitants on a large territory with important differences in term of geography and urbanism. The data gather the recording of daily measurements of five environmental variables, namely three pollutants (PM10, NO2, O3) and two meteorological factors (pressure and temperature) over six years. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing need of methods to summarize and understand them. For this purpose, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each co-cluster a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler, is proposed for model inference, as well as a model selection criteria for choosing the number of co-clusters. The application of the proposed co-clustering algorithm on environmental data of the Région Sud allowed to divide the region composed by 357 zones in six macro-areas with common exposure to pollution. We showed that pollution profiles vary accordingly to the seasons and the patterns are conserved during the 6 years studied. These results can be used by local authorities to develop specific programs to reduce pollution at the macro-area level and to identify specific periods of the year with high pollution peaks in order to set up specific prevention programs for health. Overall, the proposed co-clustering approach is a powerful resource to analyse multivariate functional data in order to identify intrinsic data structure and summarize variables profiles over long periods of time.

International audience ; Nowadays, air pollution is a major treat for public health, with clear links with many diseases, especially cardiovascular ones. The spatio-temporal study of pollution is of great interest for governments and local authorities when deciding for public alerts or new city policies against pollution raise. The aim of this work is to study spatio-temporal profiles of environmental data collected in the south of France (Région Sud) by the public agency AtmoSud. The idea is to better understand the exposition to pollutants of inhabitants on a large territory with important differences in term of geography and urbanism. The data gather the recording of daily measurements of five environmental variables, namely three pollutants (PM10, NO2, O3) and two meteorological factors (pressure and temperature) over six years. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing need of methods to summarize and understand them. For this purpose, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each co-cluster a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler, is proposed for model inference, as well as a model selection criteria for choosing the number of co-clusters. The application of the proposed co-clustering algorithm on environmental data of the Région Sud allowed to divide the region composed by 357 zones in six macro-areas with common exposure to pollution. We showed that pollution profiles vary accordingly to the seasons and the patterns are conserved during the 6 years studied. These results can be used by local authorities to develop specific programs to reduce pollution at the macro-area level and to identify specific periods of the year with high pollution peaks in order to set up specific prevention programs for health. Overall, the proposed co-clustering approach is a powerful resource to analyse multivariate functional data in order to identify intrinsic data structure and summarize variables profiles over long periods of time.

International audience ; Nowadays, air pollution is a major treat for public health, with clear links with many diseases, especially cardiovascular ones. The spatio-temporal study of pollution is of great interest for governments and local authorities when deciding for public alerts or new city policies against pollution raise. The aim of this work is to study spatio-temporal profiles of environmental data collected in the south of France (Région Sud) by the public agency AtmoSud. The idea is to better understand the exposition to pollutants of inhabitants on a large territory with important differences in term of geography and urbanism. The data gather the recording of daily measurements of five environmental variables, namely three pollutants (PM10, NO2, O3) and two meteorological factors (pressure and temperature) over six years. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing need of methods to summarize and understand them. For this purpose, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each co-cluster a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler, is proposed for model inference, as well as a model selection criteria for choosing the number of co-clusters. The application of the proposed co-clustering algorithm on environmental data of the Région Sud allowed to divide the region composed by 357 zones in six macro-areas with common exposure to pollution. We showed that pollution profiles vary accordingly to the seasons and the patterns are conserved during the 6 years studied. These results can be used by local authorities to develop specific programs to reduce pollution at the macro-area level and to identify specific periods of the year with high pollution peaks in order to set up specific prevention programs for health. Overall, the proposed co-clustering approach is a powerful resource to analyse multivariate functional data in order to identify intrinsic data structure and summarize variables profiles over long periods of time.