Title: Citizen science: Trans-expertise partnerships and heterogeneous networks for distributed knowledge production
Author(s): Tyson Vaughan
Affiliation: Cornell University
Presented At: STiS 2008
Primary Topic Area: Innovation Studies
Programs of scientific research employing the “citizen science” model, in which large numbers of “lay” participants perform mundane but crucial research tasks, have proliferated massively in the past decade, and more continue to commence operations every week. The rate and scale of this pullulation imply a historically significant trend in the evolution of scientific research and communication, but so far it has received scant attention from scholars in the field of science and technology studies. The nature of its significance, and how to understand it, remain open questions.
Citizen science projects span a range of disciplines and objectives, from astronomy to ornithology, from “pure research” to public engagement, informal education and political activism. The purpose of this paper is to situate such projects within a constellation of research programs and methodologies in which the circulation of information, practices and discipline within heterogeneous networks — comprising professional scientists, technicians, non-expert volunteers, computers, instruments, environments, user interfaces, data, etc. — enable the distributed production of knowledge. Specifically, citizen science will be compared and contrasted with open-source networks, conventional laboratories, social science surveys, clinical trials, lay epidemiology, “crowdsourcing” and the amateur-professional networks traditionally found in fields such as astronomy and ornithology.
It is hoped that such comparisons may provide starting points for answering a number of fundamental questions. What metaphor, analogy or framework best helps us to understand these projects and how they work? Are these projects economies, (actor-) networks, ecologies? What sorts of scientific projects and phenomena are amenable to the citizen science approach? What does this approach enable scientists and participants to achieve? And more broadly, what can these comparisons tell us about expertise, lay-expert partnerships and distributed knowledge production in general?