multi-sited ethnography

Intelligences

Toward Donut-Centered Design: A Design Research Toolkit for the 21st Century

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CHRISTOPHER A. GOLIAS Google [s2If is_user_logged_in()]DOWNLOAD PDF [/s2If] [s2If current_user_can(access_s2member_level1)] [/s2If] The social and ecological challenges of the 21st century require a design research process that contributes to viable economic solutions. This paper proposes donut centered design as a hybrid of service design and ecological design that works within the donut economic model. It describes how private and public sector ethnographers can weld the best of these two processes by providing a holistic, empirical research foundation that seeks to provide distributed service innovation value to all within the limits of the planet. Donut-centered design addresses lacunae of the current innovation models by advocating multi-site assessments, multi-species ethnography, ecosocial blueprints and holistics metrics as important components of a regenerative design research practice. [s2If current_user_is(subscriber)] Become a member to access our video library. Learn More. [/s2If] [s2If !is_user_logged_in()] FREE...

Perspectives

The Ethnographic Lens: Perspectives and Opportunities for New Data Dialects

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by ELIZABETH CHURCHILL, Director of User Experience, Google Concern with “big data” and ”data at scale” has pervaded many conversations in the past few years. Conversations orient around what “big data” really means, around the relationship of “small data” to “big data”, and around the relationship between “local” data and “global”, aggregate data. In the EPIC context, conversations veer towards anxiety regarding the relationship of ethnographic practice to quantitative data and whether the future of ethnography as an area of interest is in jeopardy. Will all questions regarding people, “users”, and markets be answered by brute force, number crunching? The answer is absolutely NO. It’s important to note that the ethnography community has a long-standing relationship with analysis of, and interpretation through, quantitative data at all scales and granularities. “Big” data and “data at scale” are not new. Stealing a snippet of Samuelle Carlson and Ben Anderson’s paper title from 2007,...