O Programa de Cidades Históricas, o turismo e a "viabilidade econômica" do patrimônio (1973-1979)

Created by the President's Office of Planning (Seplan) in 1973, the Program for the Revitalization of historical cities (PCH) had as its main goal the preservation and the economical development of some of Brazil's heritage urban complexes. The actions carried out by this program will be analyzed here as part of a broader phenomenon in the history of Brazilian cultural heritage preservation, namely an encouragement to transform preserved sites into tourist attractions. In order to do so, I analyze documents, lectures, and speeches delivered by some of the essential players in the activities of heritage preservation and the promotion tourism, in addition to periodicals and legislation from the time period.Criado pela Secretaria de Planejamento da Presidência da República (Seplan), no ano de 1973, o Programa de Revitalização das Cidades Históricas (PCH) tinha como meta principal a preservação e o desenvolvimento econômico de alguns dos conjuntos urbanos brasileiros patrimonializados. As ações desenvolvidas por esse programa serão aqui analisadas como parte de um fenômeno mais amplo da história da preservação do patrimônio cultural brasileiro, o estímulo à transformação dos sítios preservados em atrações turísticas. Para tanto, foram analisados documentos, palestras e discursos de personagens fundamentais para as atividades de salvaguarda do patrimônio e de promoção do turismo, além de periódicos e legislação do período

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

Decreto n. 282, 27 fev. 1902, RJ.

Este arquivo foi publicado em Gazeta de Noticias, anno XXVIII, nº 60, 1º de Março de 1902, p. 2 e 3. Disponível em http://memoria.bn.br/DocReader/docreader.aspx?bib=103730_04. (Contribuição da equipe do Laboratório de Pesquisa em História da Educação Matemática (LaPHEM), Vassouras – RJ).Dá regulamento ao ensino profissional. Districto Federal

Decreto n. 281, 27 fev. 1902, RJ.

Este arquivo foi publicado em Gazeta de Noticias, anno XXVIII, nº 60, 1º de Março de 1902, p. 2. Disponível em http://memoria.bn.br/DocReader/docreader.aspx?bib=103730_04. (Contribuição da equipe do Laboratório de Pesquisa em História da Educação Matemática (LaPHEM), Vassouras – RJ).Dá Regulamento para o Pedagogium

Data resulting from tests relating to Digital media impact on reading

Results for tests conducted with undergraduated and graduated students for the "Digital media impact on reading" researc