Knowledge representation in scruffy worlds : an ethnography of semiotic infrastructure design work

Abstract

The research presented in this dissertation is based on four years of ethnographic fieldwork with diverse data communities – including the Semantic Web community and the human services informatics community. It is also based, in part, on reflections of my own involvement in a project to design a digital humanities platform - the Platform for Experimental Collaborative Ethnography (PECE). Through interviewing, archival work, and experimental design projects, I aimed to unpack how semiotic technologists in these communities learned to endure the limits of knowledge representation.

Focused at the scale of meaning-making, the dissertation contributes to literature (situated at the intersection of Science and Technology Studies and Information Studies) theorizing the history and politics of information infrastructures. It also furthers understanding of social practice and politics in big data contexts, demonstrating how data infrastructure designers learn to bring different assumptions, logics, and politics to their work. Finally, the dissertation recommends pedagogy for training the next generation of data and information scientists to recognize, communicate, and creatively endure the limits of representing knowledge in diverse data domains.

I argue that, while semiotic technologists bring particular language ideologies to their design work, they are often faced with competing injunctions – for instance, to make infrastructures more flexible for characterizing different interpretations of data’s meaning, or to make them more structured so that diverse communities sharing data can use them to align their language. I follow semiotic technologists as they approach these tradeoffs, examining how their ideas about language and meaning shift as they learn to work in domains where the meaning of data is messy or “scruffy.” I show how they learn to assess the challenges of representing meaning in diverse data communities and how they design strategically and experimentally to address these challenges. In doing so, I argue that what it means to be an expert in encoding meaning is constantly evolving – stabilizing in particular times and contexts.

This dissertation examines the history, culture, and expertise of data infrastructure design work. More specifically it narrates how a community of researchers and practitioners that I refer to as semiotic technologists design data infrastructures to encode and represent the meaning of data. I demonstrate how semiotic technologists' ideas about language (about how meaning takes shape and evolves) animate how they approach the design of these data infrastructures, impacting how the infrastructures eventually order and represent data. Therefore, I refer to the infrastructures I study in the dissertation as semiotic infrastructures.