This article explores the evolution of audience analytics in filmmaking in the context of digital platforms and AI. We propose the term recalcitrant audiencing to describe a mode of audience engagement that resists confirming filmmakers' assumptions, instead challenging dominant ideas about who the audience is or should be. In place of a docile, knowable audience, we trace the emergence of one that is intentionally recalcitrant- ambivalent, heterogeneous, and constructed to be troubling.

The chapter introduces the Data Diamond as a heuristic tool for analyzing and mapping data situations. We illustrate the model with a case from Helsingør Municipality, where semantic analysis was used to understand student well-being as part of an MDO project.

This pre-print introduces “Synthetic Interlocutors” for ethnographic research. Synthetic Interlocutors are chatbots ingested with ethnographic textual material (interviews and observations) by using Retrieval Augmented Generation (RAG). We integrated an open-source large language model with ethnographic data from three projects to explore two questions: Can RAG digest ethnographic material and act as ethnographic interlocutor? And, if so, can Synthetic Interlocutors prolong encounters with the field and extend our analysis?.

This paper provides a theoretical alternative to the prevailing perception of machine learning as synonymous with speed and efficiency. Inspired by ethnographic fieldwork and grounded in pragmatist philosophy, we introduce the concept of “data friction” as the situation when encounters between held beliefs and data patterns posses the potential to stimulate innovative thinking.