Computational Ethnography: Scaling Community-Based Participatory Research and Planning

Abstract

Using ethnographic methods in community-based participatory research (CBPR) fosters mutual trust and collaboration between researchers and community members. However, scaling these methods is challenged by limited researcher resources and community member involvement. This paper introduces computational ethnography (CE), which uses computational assists to deliver rich insights. We argue that traditional ethnography, even with computational techniques, is insufficient for scaling effectively. Our Machine–Readable Co-design (MaRC) toolkit, a CE method, leverages human–machine teaming to address the collection and analysis of community stories at scale in CBPR. MaRC integrates AI/ML technologies to support effective community co-planning. In this study, two researchers used MaRC to capture and analyze over 100 community stories. While researchers spent over 120 hr across 2 months manually measuring and recording data, MaRC computed each story within 8 to 10 minutes, achieving an 84% concordance rate with our manual, baseline analysis. MaRC demonstrates significant potential for scaling future CBPR efforts.