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Titlebook: Indigenous Perceptions of the End of the World; Creating a Cosmopoli Rosalyn Bold Book 2019 The Editor(s) (if applicable) and The Author(s)

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書目名稱Indigenous Perceptions of the End of the World
副標題Creating a Cosmopoli
編輯Rosalyn Bold
視頻videohttp://file.papertrans.cn/464/463658/463658.mp4
概述One of the first volumes to seriously consider indigenous perspectives on climate change alongside scientific narratives.Collects indigenous Latin American perspectives on climate change, consumption,
叢書名稱Palgrave Studies in Anthropology of Sustainability
圖書封面Titlebook: Indigenous Perceptions of the End of the World; Creating a Cosmopoli Rosalyn Bold Book 2019 The Editor(s) (if applicable) and The Author(s)
描述.This edited volume constructs a ‘cosmopolitics’ of climate change, consulting small-scale sustainable communities on whether the world is ending and why, and how we can take action to prevent it. By comparing scientific and indigenous accounts of the same phenomenon, contributors seek to broaden Western understandings of what climate change constitutes. In this context, existing cosmologies are challenged, opening spaces for hegemonic narratives to enter into conversation with the non-modern and construct ‘worlds otherwise’—situations of world change and renewal through climate change. Bold brings together perspectives from Central America, Mexico, the Amazon, and the Andes to converse with scientific narratives of climate change and create cracks that bring new worlds into being for readers..
出版日期Book 2019
關(guān)鍵詞Anthropocene; ontological perspectives; indigenous peoples; non-modern people; catastrophe; apocalypse; mo
版次1
doihttps://doi.org/10.1007/978-3-030-13860-8
isbn_softcover978-3-030-13862-2
isbn_ebook978-3-030-13860-8Series ISSN 2945-6657 Series E-ISSN 2945-6665
issn_series 2945-6657
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Rosalyn Bolde physiology from available patient data and using a mixed integer optimization approach to control blood glucose levels in real-time using the inferred models. First, our approach learns neural networks to predict the future blood glucose values from given data on insulin infusion and their resulti
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