Hyperimage Index: Rendering Research On Algorithmic Image Systems Through Aggregating, Mapping and Collective Indexing

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Abstract

Image has gone hyper, can research catch up? This essay proposes collective indexing as an alternative to academic publishing for rendering research on fast-changing and larger-than-human subjects such as algorithmic images. Following the introduction of notions of network and scale in my research, the essay articulates the value of collective indexing while mapping out contemporary examples. Collective indexing produces new ways of knowledge making and community building, as well as new forms of research aesthetics apt for addressing the distributed nature of algorithmic image systems.
Original languageEnglish
Pages (from-to)66-81
JournalA Peer Review Journal About
VolumeRendering Research
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

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