IRIS is pleased to announce that a research team led by James Evans at the University of Chicago has won the 2020 IRIS Researcher Award.

The awards are supported by the Alfred P. Sloan Foundation, and have been given out since 2018. For a listing of previous awardees, visit iris.isr.umich.edu/awards-fellowships.

Research Team: James Evans (pictured at right) is the Director of Knowledge Lab, Professor of Sociology, Faculty Director of the Computational Social Science program, and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. He is an External Professor at the Santa Fe Institute. His research is centered on collective systems of thinking and knowing: the origin of ideas, the accumulation of certainty, and the social structure of innovation. His is joined by Brendan Chambers (left), postdoctoral scholar in the Knowledge Lab and two-time National Science Foundation Fellow; and Donghyun Kang (center), PhD student focused on the social processes of dissonance and consensus in interdisciplinary research.
Project Title: Impact of strategic funding on research type and content
Abstract: Academic ecologies need a diverse set of research programs: programs that receive few citations, but publish important research with long‐term effects; that publish anchoring paradigmatic work, or conversely, disruptive innovative work; programs driving interdisciplinary exchange. All of these qualities are important, even if sometimes at the cost of a lower H‐index or less immediate exposure. Simplified scalar measures fail to capture these multidimensional cultures of success. Stakeholders need richer signals so they can allocate strategic funding in a manner sensitive to existing institutional and organizational strengths. We propose, first, to refine an innovative framework rooted directly in published textual products, with multiple dimensions of measurement that can better accommodate diverse strategies for research impact. Linguistic influence within the broader publication record is a crucial and overlooked measure of research outcomes. To measure these implicit flows, we propose to map the semantic geography of collective academic production by compressing encodings of published abstracts, using state‐of‐the‐art deep contextual embedding methods. Our team has already deployed these methods at scale and validated their sensitivity to semantic structure—sensitivity not just to individual words, but to their composed, contextual meaning. Second, using our measures based on typologies within semantic space, we will measure significant factors at the coupling between funding portfolios and research outcomes. Finally, adapting methods to study causal influence in biological networks employed by members of our team, we will filter measured associations to identify a set of likely causal factors by which distinct funding portfolios support archetypical research strategies and outcomes.