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To make working in this new middle ground between science and policy more tenable, changes on both sides must occur. This could mean a new understanding of what kinds of knowledge are suitable for academic publication. Alfsen-Norodom says that those in the policy world must become “research animals,” too: “Knowledge is not exclusively limited to the university. There is local knowledge from indigenous people, especially on the environment. People like myself also have knowledge; it may not be scientific knowledge, but it has value to being published.”
A more robust and widely drawn map of publications could help in initiating and enhancing such bridge-making partnerships, as institutional founders and funders could more easily see the convergent themes of disparate scientists’ and policymakers’ work. But the real value of such expertise would be in guiding the efforts of an institution’s researchers in unison to address a multifaceted problem. With experts trained in connecting real-world concerns to new maps of science, such centers would better be able to see the degree to which their scientific ideas are spread and adopted by policymakers, educators, journalists, and everyday citizens.
Many of the components for a holistic and robust science-transfer system already exist, albeit in disconnected corners of the academic and public spheres. Disciplinary organizations undertake public outreach efforts on blogs and websites. Cross-disciplinary bodies exist on both sides, from the European Association of Science and Technology Transfer Professionals to the US National Science Foundation and its analogs in other countries.
The technological foundations are similarly balkanized. There are standards for open access publishing, the sharing of notes and experimental data through systems like OpenWetWare, and even some preliminary science maps. Take STS pioneer Bruno Latour’s current project, MACOSPOL (Mapping Controversies on Science for Politics). MACOSPOL is an international collaboration of sociologists, computer and network scientists, data visualizers, and others, bent on addressing one facet of science transfer: how ambiguous scientific information is assimilated by the public, and the political risks inherent in that discourse. But like scientific disciplines themselves, the ability to integrate the information contained within and turn it into new knowledge is scattershot.
All these developments point to a revolution stirring in the sciences, and the disparate, newly emerging methods for the collection, sharing, and evaluation of scientific data are ethereal nodes in its rapidly solidifying outlines. Each day the connections between them strengthen and grow, hinting at the eventual formation of common standards that could unify the tools of science mapping and accelerate the process of science transfer.
When and if this occurs, science as we know it will be transformed. And while such radical alterations in the evaluation and communication of scientific information may eliminate many of the difficulties that now stifle innovation and prevent a greater public engagement with science, they will also introduce new instabilities into the ecosystem of science. We are seeing some of them already.
The landscape of science is undoubtedly changing. Many of these changes—the decline of outdated print-publication models, the rise of interdisciplinary activity, the rampant acceleration of data production in all fields—appear inevitable, forced by technological developments that alter the infrastructure of science. Others—the ethic of open access, the emergence of a holistic discipline of science mapping, and the semi-automated distillation of true knowledge from endless data—remain in the uncertain realm of possibility. These are only a few of the myriad paths forward.
The ultimate outcome of this grand unfolding experiment is unclear, but it seems unquestionable that the progression from experiment to conclusion to application can and will be accelerated. Far less certain is whether this change will be accompanied by a faster maturation of data into knowledge and then wisdom. But bringing greater scientific rigor to the study of science itself can only enhance our chances of success.
Originally published January 27, 2011
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