Chamados para fora: a igreja evangélica nos flash mobs na cena carioca

Este artigo tem por objetivo discutir a apropriação do espaço público pelas igrejas evangélicas como estratégia de comunicação da mensagem evangelística, na cena internacional e brasileira, utilizando-se de atividades performático-musicais, os flash mobs. Para tal, inicialmente se procurará definir os termos igreja e flash mob, na intenção de relacioná-los. Centralmente, será feita uma análise do panorama mundial até uma peça audiovisual, como estudo de caso, sob o olhar das ciências humanas. Trata-se de um flash mob realizado em coautoria, envolvendo três igrejas de denominações díspares, no bairro de Campo Grande (Zona Oeste), bairro mais populoso da cidade do Rio de Janeiro (IBGE, 2010). Sob o olhar da cultura gospel (CUNHA, 2007), será observada a apropriação da rua pelas igrejas evangélicas, utilizando-se dos flash mobs como intenção enunciativa de comunicar as boas novas

EM PAUTA: POLÍTICAS DE COMUNICAÇÃO NAS UNIVERSIDADES PÚBLICAS

Nas palavras de Simon Schartzmann, “a universidade é o lugar ideal para a pesquisa. Se a atividade de pesquisa não for conhecida e não tiver apoio da sociedade, ela morre”. E se a pesquisa morre, não há sombra de desenvolvimento nacional nas diversas esferas a médio e curto prazos. Considerando que as universidades públicas têm sido atacadas frequentemente no Brasil seja por enviesamentos ideológicos que não se coadunam com o saber ou com a democracia, seja por interesses duvidosos, é preciso que estas instituições discutam meios de sua afirmação como instituição de Estado que estão a serviço da sociedade e não a governos passageiros. Um desses meios é a instauração de políticas de comunicação. Para discutir o assunto, o Prof. Dr. Wilson Bueno foi convidado para ceder uma entrevista

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost