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Aesthetic Indigenous Forms: Generative AI Imagery Dataset
The past, present, and future of our city communities in the United States exist within colonization's ongoing violence - a perpetual state of lived aftermath to stolen lands, white supremacy, genocide, slavery, and anthropogenic climate change. Our land and water relations remember how we treat them with sewage, chemicals, and trash, and they influence the artistic expressions and world's of Black Philadelphia writers, philosophers, artists. The Aesthetic Indigenous Forms Dataset includes 29 generative artificial intelligence (Gen AI) images, historical research, and curatorial prose that complicates the stories of climate racism, Indigeneity, and climate change that Philadelphia's public art and environmental histories tell. This data is integrated within the Post Colonial Dreams Museum, one of two distinct, yet interconnected virtual museums in Relational Possibilities: A Remix of Aesthetic Forms Through Indigeneity and Blackness. Curated by Dana Reijerkerk, B.A., M.I.S.The Creative CoLab Project: Relational Possibilities, LEADING Fellow 2023-2024. This work is licensed under: CC BY-NC-ND 4.0.
Climate Change, Public Art Data, AI Images, Environmental Data, Climate Racism, Black art, Indigenous art
Climate Change, Public Art Data, AI Images, Environmental Data, Climate Racism, Black art, Indigenous art
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
