New Methods for Measuring Expert Influence

This research investigates how and when scientists and other technical experts have affected decisions related to science, technology, and innovation (STI) decisions of national importance, such as research security, science education, and public health. Decision makers increasingly rely on scientists and other technical experts to inform U.S. national STI decisions.

However, the influence of science and scientists in public decision making remains difficult to evaluate due to the wide range of evidence used and the delay between ideas and outcomes. The research will focus on the role of advisors and entrepreneurs in advising and coordinating STI research and development (R&D). The goal is to inform the science decision making system and increase the appropriate uptake of scientific data and analysis into STI decision making.

A more effective U.S. science decision making system will lead to improved outcomes for the American public and strengthen U.S global leadership in STI R&D.

This research draws on complementary methods in science of science, the digital humanities, and computational sociology to build a novel methodology for identifying and measuring expert influence on decision making related to STI. Utilizing a large corpus of digital textual records, the research team uses a relational database (RDB), which connects individual actors and institutions to documents and outcomes through linked metadata, to trace systematically the decision-making process across time.

The analysis of the RDB is complemented by semi-structured and oral history interviews. The interviews and oral histories are analyzed using computational grounded theory to identify patterns in interview responses, providing a means to validate findings from the text analysis.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.