14 research outputs found
Fault lines after the Cold War: the vertical expansion of the concept of security, securitization and human security
The paper approaches the “vertical expansion of the concept of security” reconstructing the debate on the concept of security within the discipline of International Relations after the Cold War. Considering that security is an “essentially contested concept”, it offers a handful of comparisons between different conceptions, which provide different accounts of “broadening” security. Barry Buzan’s Securitization approach was the first to engage seriously the challenges of “broadening” security in IR. For its merits, however, Buzan’s communitarian ontology poses a problem to “broadening” security, as it reiterates the state as the gatekeeper of protection and as the authoritative site for defining existential threats. In this sense, in spite of all its overriding ambiguity, Human Security provides a better alternative for the “vertical expansion of the concept of security” than securitization. The paper, therefore, considers the respective contributions of securitization and human security to the debate on the vertical expansion of security under the light of the relationship between states and human beings
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
[pt] MUDANÇAS INSTITUCIONAIS NAS ATIVIDADES RELATIVAS ÀS OPERAÇÕES DE MANUTENÇÃO DA PAZ DO SISTEMA ONU DO PÓS-GUERRA FRIA: ADAPTAÇÃO VERSUS APRENDIZADO
O presente trabalho se propõe a aferir a mudança
institucional que teve
lugar nas atividades da Organização das Nações Unidas
(ONU) relacionadas
com as Operações de Manutenção da Paz, no período 1992-
2000. A abordagem
levada a cabo se localiza no encontro da disciplina das
Relações Internacionais
com outras Ciências Sociais, através dos autores Ernst
Haas e Anthony
Giddens. Na Modernidade, organizações sociais, como a ONU,
levam a cabo
contínuo monitoramento, reflexivo, de suas próprias ações,
na busca por
solucionar problemas de cuja solução são incumbidos. Nessa
dinâmica de
monitoramento reflexivo, as organizações sociais podem -
ou não - aprender
com suas experiências pregressas.The present research intends to evaluate institutional
change that had
taken place within United Nations (UN) activities related
to Peacekeeping
operations, from 1992 to 2000. The following approach is
located on the
interface between International Relations and other Social
Sciences, through the
theoretical contributions of Ernst Haas and Anthony
Giddens. In Modernity,
social organizations (such as UN) continuously monitor its
own behavior -
reflexively - seeking for solutions for problems to be
solved. Amidst this
dynamics of reflexive monitoring, social organizations
may - or may not - learn
from its previous experiences