Re-ordering Epistemic Living Spaces: On the Tacit Governance Effects of the Public Communication of Science

  • Ulrike FeltEmail author
  • Maximilian Fochler
Part of the Sociology of the Sciences Yearbook book series (SOSC, volume 28)


Media have become an important arena of and actor in the co-evolution of science and society. Medialization re-shapes the professional and public identities of scientists, who are increasingly expected to consider communication activities as part of their professional role. Our interest in this contribution lies in tracing how medialization impinges on key processes and symbolic orders within research. The central thesis of this chapter is that medialization does not only affect the context in which research happens, such as its financial organisation or regulation, but that media have become deeply involved in shaping scientists’ epistemic living spaces. Our approach starts from a person-centred perspective, and asks what consequences the proliferation of communication activities and of media representations of research has for academic scientists and their ways of living and working in research. How does scientists’ increasing engagement in communication activities feed back into research and influence their identities and practices as scientists? How do these communication activities affect the social and symbolic orders that define what it means to do research today? And how do they both tacitly govern research environments as well as researchers’ self-understanding? By starting from these questions about how researchers’ ways of living and working in science are affected by medialization on a micro-level we aim to work towards conclusions about the more systemic effects of medialization, both in science as well as in its relation to other societal actors and systems.


Life Science Scientific Practice Scientific Career Senior Scientist Boundary Work 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper is based on empirical work done in several research projects – for details please see the section on material and methods. The authors acknowledge the contribution of all colleagues involved in these projects, and would like to thank the editors and reviewers of this volume as well as the participants of the yearbook conference in Bielefeld for their comments and suggestions. We are also grateful to Martha Kenney for the final language editing.


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© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.University of Vienna
  2. 2.Department of Social Studies of ScienceUniversity of ViennaWienAustria

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