The Experts below are selected from a list of 41022 Experts worldwide ranked by ideXlab platform
Ashok Gulati - One of the best experts on this subject based on the ideXlab platform.
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:"Policies of devolving management of resources from the state to user groups are premised upon the assumption that users will organize and take on the necessary management tasks. While experience has shown that in many places users do so and are very capable, expansion of co-management programs beyond initial pilot sites often shows that this does not happen everywhere. Yet, much is at stake in this, with more widespread adoption of irrigation management transfers and other forms of community-based resource management. It is therefore important to move beyond isolated case studies to comparative analysis of the conditions for Collective action. This paper identifies factors affecting organization of water users' associations, and Collective action by farmers in major canal irrigation systems in India, based on quantitative and qualitative analysis of a stratified sample of 48 minors in four irrigation systems (two each in Rajasthan and Karnataka). Using key variables suggested by the theoretical and case study literature, the study first examines the conditions under which farmers are likely to form formal or informal associations at the level of the minor (serving several watercourses, and one or more villages). Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, and in sites with religious centers and potential leadership from college graduates and influential persons, but head/tail location does not have a major effect. We then examine factors affecting two different forms of Collective action related to irrigation systems: Collective Representation and maintenance of the minors. Lobbying activities are not more likely where there are organizations, but organizations do increase the likelihood of Collective maintenance work." Author' Abstract
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:Abstract Policies of devolving management of resources generally assume that users will organize and take on the necessary management tasks. Experience with comanagement programs shows that this does not happen everywhere. This paper identifies factors affecting organization and Collective action among water users in major canal irrigation systems in India. Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, with religious centers and potential leadership from college graduates and influential persons. Water users' organizations increase the likelihood of Collective maintenance work by farmers, but do not affect the likelihood of Collective Representation, or lobbying activities, which seem to happen more spontaneously.
Ruth Meinzendick - One of the best experts on this subject based on the ideXlab platform.
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:"Policies of devolving management of resources from the state to user groups are premised upon the assumption that users will organize and take on the necessary management tasks. While experience has shown that in many places users do so and are very capable, expansion of co-management programs beyond initial pilot sites often shows that this does not happen everywhere. Yet, much is at stake in this, with more widespread adoption of irrigation management transfers and other forms of community-based resource management. It is therefore important to move beyond isolated case studies to comparative analysis of the conditions for Collective action. This paper identifies factors affecting organization of water users' associations, and Collective action by farmers in major canal irrigation systems in India, based on quantitative and qualitative analysis of a stratified sample of 48 minors in four irrigation systems (two each in Rajasthan and Karnataka). Using key variables suggested by the theoretical and case study literature, the study first examines the conditions under which farmers are likely to form formal or informal associations at the level of the minor (serving several watercourses, and one or more villages). Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, and in sites with religious centers and potential leadership from college graduates and influential persons, but head/tail location does not have a major effect. We then examine factors affecting two different forms of Collective action related to irrigation systems: Collective Representation and maintenance of the minors. Lobbying activities are not more likely where there are organizations, but organizations do increase the likelihood of Collective maintenance work." Author' Abstract
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:Abstract Policies of devolving management of resources generally assume that users will organize and take on the necessary management tasks. Experience with comanagement programs shows that this does not happen everywhere. This paper identifies factors affecting organization and Collective action among water users in major canal irrigation systems in India. Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, with religious centers and potential leadership from college graduates and influential persons. Water users' organizations increase the likelihood of Collective maintenance work by farmers, but do not affect the likelihood of Collective Representation, or lobbying activities, which seem to happen more spontaneously.
K V Raju - One of the best experts on this subject based on the ideXlab platform.
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:"Policies of devolving management of resources from the state to user groups are premised upon the assumption that users will organize and take on the necessary management tasks. While experience has shown that in many places users do so and are very capable, expansion of co-management programs beyond initial pilot sites often shows that this does not happen everywhere. Yet, much is at stake in this, with more widespread adoption of irrigation management transfers and other forms of community-based resource management. It is therefore important to move beyond isolated case studies to comparative analysis of the conditions for Collective action. This paper identifies factors affecting organization of water users' associations, and Collective action by farmers in major canal irrigation systems in India, based on quantitative and qualitative analysis of a stratified sample of 48 minors in four irrigation systems (two each in Rajasthan and Karnataka). Using key variables suggested by the theoretical and case study literature, the study first examines the conditions under which farmers are likely to form formal or informal associations at the level of the minor (serving several watercourses, and one or more villages). Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, and in sites with religious centers and potential leadership from college graduates and influential persons, but head/tail location does not have a major effect. We then examine factors affecting two different forms of Collective action related to irrigation systems: Collective Representation and maintenance of the minors. Lobbying activities are not more likely where there are organizations, but organizations do increase the likelihood of Collective maintenance work." Author' Abstract
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what affects organization and Collective action for managing resources evidence from canal irrigation systems in india
World Development, 2002Co-Authors: Ruth Meinzendick, K V Raju, Ashok GulatiAbstract:Abstract Policies of devolving management of resources generally assume that users will organize and take on the necessary management tasks. Experience with comanagement programs shows that this does not happen everywhere. This paper identifies factors affecting organization and Collective action among water users in major canal irrigation systems in India. Results indicate that organizations are more likely to be formed in larger commands, closer to market towns, with religious centers and potential leadership from college graduates and influential persons. Water users' organizations increase the likelihood of Collective maintenance work by farmers, but do not affect the likelihood of Collective Representation, or lobbying activities, which seem to happen more spontaneously.
Ivo D Dinov - One of the best experts on this subject based on the ideXlab platform.
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predictive big data analytics a study of parkinson s disease using large complex heterogeneous incongruent multi source and incomplete observations
PLOS ONE, 2016Co-Authors: Ivo D Dinov, Ben Heavner, Ming Tang, Gustavo Glusman, Kyle Chard, Mike Darcy, Ravi Madduri, Cathie Spino, Carl KesselmanAbstract:Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective Representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.
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predictive big data analytics a study of parkinson s disease using large complex heterogeneous incongruent multi source and incomplete observations
PLOS ONE, 2016Co-Authors: Ivo D Dinov, Ben Heavner, Ming Tang, Gustavo Glusman, Kyle Chard, Mike Darcy, Ravi Madduri, Judy Pa, Cathie SpinoAbstract:Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective Representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.
Patricia Crifo - One of the best experts on this subject based on the ideXlab platform.
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la participation des salaries du partage d information a la codetermination
2019Co-Authors: Antoine Reberioux, Patricia CrifoAbstract:"Promu par les innovations manageriales, l'engagement des salaries au travail trouve une issue logique dans leur participation aux decisions de l'entreprise. Cette participation repond en outre a l’aspiration des salaries et de leurs representants a intervenir sur les conditions de travail, a discuter des questions d’emploi et de remuneration, ainsi que des choix strategiques de leur entreprise. Certains a discretion des directions, d’autres obligatoires, les dispositifs de participation que recense et analyse cet ouvrage revetent des formes diverses : droit economique du comite d’entreprise, negociation Collective, Representation au conseil d’administration, etc. Comment ces canaux s’articulent-ils ? Comment contribuent-ils a l’amelioration des conditions de travail, a la transition ecologique, a la responsabilite sociale des entreprises ? Que peut-on en attendre, en termes de competitivite ? Un tour d’horizon synthetique et critique, alors que la loi PACTE du 22 mai 2019 prescrit une plus grande participation des salaries au capital et aux decisions strategiques des entreprises." (source editeur)
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worker involvement from information sharing to codetermination la participation des salaries du partage d information a la codetermination
Post-Print, 2019Co-Authors: Antoine Reberioux, Patricia CrifoAbstract:"Promu par les innovations manageriales, l'engagement des salaries au travail trouve une issue logique dans leur participation aux decisions de l'entreprise. Cette participation repond en outre a l'aspiration des salaries et de leurs representants a intervenir sur les conditions de travail, a discuter des questions d'emploi et de remuneration, ainsi que des choix strategiques de leur entreprise. Certains a discretion des directions, d'autres obligatoires, les dispositifs de participation que recense et analyse cet ouvrage revetent des formes diverses : droit economique du comite d'entreprise, negociation Collective, Representation au conseil d'administration, etc. Comment ces canaux s'articulent-ils ? Comment contribuent-ils a l'amelioration des conditions de travail, a la transition ecologique, a la responsabilite sociale des entreprises ? Que peut-on en attendre, en termes de competitivite ? Un tour d'horizon synthetique et critique, alors que la loi PACTE du 22 mai 2019 prescrit une plus grande participation des salaries au capital et aux decisions strategiques des entreprises." (source editeur)