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For centuries, scientists and scholars have measured the influence of individuals and discoveries through citations, a crude statistic subject to biases, politics and other distortions. A new paper led by the Knowledge Lab at the University of Chicago describes a different way to keep score in science-a more direct measure of how influential ideas ripple out across scholarship and culture. The computational model throws the spotlight onto work that changed the path of science but has remained underappreciated. The same approach also can be adapted to trace influence in other areas where no culture of citation exists, such as literature or music, said the authors of the paper published last week in Proceedings of the National Academy of Sciences . "We're measuring how much scientists' and scholars' writings influence discussion of ideas in the future," said James Evans , director of Knowledge Lab and professor of sociology at UChicago. "Influence is a politicized process; those who get the influence, get the credit, and those who get the credit get the capital to do the next big thing. This is the first time we have a tightened ability to identify influence, and also to diagnose social and strategic influences on citing behavior." The new paper complements previous Knowledge Lab research using computational and machine learning approaches on massive collections of text, grants, reviews, citations and scientific data to study how discoveries form, evolve and become widely accepted.