This talk is presented by the Center for Computational and Data Science (CCDS) at the School of Information Studies (iSchool).
Misha Teplitskiy, Assistant Professor of Information, School of Information, University of Michigan
The peer review process of academic journals is a key gatekeeper between the new ideas that researchers generate and the new ideas that become vetted and visible. Despite decades of debate, little is known about whether peer review favors novel ideas or incremental ones. We address this question using the peer review files of ~20K submissions to two journals in biology, one field-leading and one middle-tier. We find that reviewers at both journals do not show a preference for or against novel ideas. However, the editors of the top journal select for novelty, making novel ideas more likely to get published. In contrast, the editors of the middle-tier journal select against novel ideas. We investigate possible explanations of the status-novelty relationship, including editors’ decision-making and composition of the pool of submissions.
Misha Teplitskiy is an Assistant Professor at the University of Michigan School of Information. His research is at the intersection of Science of Science and Sociology of Organizations. Misha studies how social and organizational factors affect scientific discovery. He focused interest in evaluating practices in science, and whether they promote or stifle innovation. Misha’s approach relies on field experiments with scientists as they conduct their work, and applying computational tools to large-scale observational data.
Current research projects include:
- Metrics in science and performativity: Do things like citation counts and impact factors proxy quality and influence, or help create them?
- Social influence among experts: How do experts influence one another’s opinions, i.e. during peer review of grant applications?
This event was published on November 2, 2020.
- Information and Library Science
- Open to
- School of Information Studies (iSchool)
- Contact Center for Computational and Data Science (CCDS) to request accommodations