Measuring the Invisible : A Data Journey of Cultural and Creative Value in Sweden

Sammanfattning

This thesis examines how cultural and creative value is made visible or invisible as it moves through the institutional system of Swedish cultural and creative industries (CCIs). Drawing on commensuration theory (Espeland & Stevens, 1998), care theory (Puig de la Bellacasa, 2017), and framing theory (Entman, 1993), the study traces the data journey through which cultural activity is classified, reported, aggregated, and represented in national statistics. It combines document analysis, visual framing analysis of statistical outputs, and semi-structured interviews with four actors operating across the system. The analysis proposes a four-loss framework identifying how value is discarded at different stages, operating simultaneously and recursively: classificatory loss, where organisations are excluded by classification codes; situated loss, where care-based work is reduced to fit reporting categories; institutional loss, where funding channels route data away from cultural statistics; and representational loss, where the resulting charts render these exclusions invisible. The interview material reveals that actors across the journey experience these processes as invisible administrative labor, identity friction, strategic self-presentation, and absorbed financial cost. At the same time, the system projects a certainty that none of its users holds. The thesis argues that this invisibility is structural, and self-reinforcing through a policy feedback loop in which statistics produced from an incomplete population generate decisions that reproduce this incompleteness. Ultimately, this thesis’ contribution to Media and Communication Studies is the demonstration that measurement systems construct what the sector is, determining whose labor counts, whose work is recognized, and whose contribution remains outside the frame. The study proposes the four-loss framework as a transferable tool for examining reporting infrastructures in other sectors, and calls for further research into alternative representational approaches capable of representing and embracing the complexity that standard metrics exclude.