The impact of Participatory Budgeting on health and wellbeing:A scoping review of evaluations

Background: Participatory budgeting (PB), citizens deliberating among themselves and with officials to decide how to allocate funds for public goods, has been increasingly implemented across Europe and worldwide. While PB is recommended as good practice by the World Bank and the United Nations, with potential to improve health and wellbeing, it is unclear what evaluations have been conducted on the impact of PB on health and wellbeing. Methods: For this scoping review, we searched 21 databases with no restrictions on publication date or language. The search term ‘participatory budget’ was used as the relevant global label for the intervention of interest. Studies were included if they reported original analysis of health, social, political, or economic and budgetary outcomes of PB. We examined the study design, analysis, outcomes and location of included articles. Findings are reported narratively. Results: From 1458 identified references, 37 studies were included. The majority of evaluations (n = 24) were of PB in South America, seven were in Europe. Most evaluations were case studies (n = 23) conducting ethnography and surveys, focussing on political outcomes such as participation in PB or impacts on political activities. All of the quantitative observational studies analysing population level data, except one in Russia, were conducted in South America. Conclusion: Despite increasing interest in PB, evaluations applying robust methods to analyse health and wellbeing outcomes are scarce, particularly beyond Brazil. Therefore, implementation of PB schemes should be accompanied by rigorous qualitative and quantitative evaluation to identify impacts and the processes by which they are realised

CONSCIÊNCIA POLÍTICA E PARTICIPAÇÃO CIDADÃ DE ESTUDANTES DE ADMINISTRAÇÃO: UM ESTUDO EXPLORATÓRIO EM UMA UNIVERSIDADE PÚBLICA NO BRASIL

Este artigo analisa a dinâmica da consciência política dos estudantes de graduação em Administração de uma universidade pública federal brasileira quanto à participação cidadã em lugares públicos participativos. A discussão se baseia no modelo de análise da consciência política para compreensão da participação em ações coletivas de Sandoval (2001). Os dados foram coletados e analisados em duas etapas, por meio de  questionários e entrevistas semiestruturadas em 2014, submetidos à análise de conteúdo (BARDIN, 2004). Os resultados revelam como justificativas citadas pelos estudantes que participam o interesse em exercer a cidadania, em melhorar as políticas públicas, o gosto por assuntos públicos e defesa de seus interesses em circunstâncias de conflito. Nos estudantes com participação mais ativa, evidenciam-se crenças, valores e expectativas societais, articuladas à eficácia política, identidade coletiva, interesses antagônicos, sentimentos de justiça e injustiça, favorecendo a vontade de agir coletivamente, devido à percepção de conexão de seus interesses com as metas e ações coletivas dos movimentos que se envolvem. Os estudantes que não participam desconfiam dos lugares públicos participativos e demonstram desinteresse pelos assuntos públicos, embora apontem um desconforto em não participar. Suas crenças, valores e expectativas societais, associadas aos sentimentos de ineficácia política dificultam o desenvolvimento da consciência política. Conclui-se que estes estudantes possuem uma consciência política de senso comum, demonstrando valores inerentes aos modismos presentes na vida cotidiana das pessoas. Já os estudantes com participação mais ativa apresentam uma consciência política de conflito, motivando-os à participação nos lugares avaliados como eficazes às suas proposições. Entretanto, o Centro Acadêmico, principal lugar de representação e participação dos interesses dos estudantes dos cursos em estudo, encontra-se sem direção e participação nas instâncias institucionalizadas na universidade

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.Comment: Contribution to Snowmass 202

Machine Learning and LHC Event Generation

International audienceFirst-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem

Machine Learning and LHC Event Generation

International audienceFirst-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem

Machine Learning and LHC Event Generation

International audienceFirst-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem