2021/05/17 by Vladlen Koltun, David Hafner, Koltun, Vladlen +1
Decision Sciences · Materials Science · #Artificial Intelligence (cs.AI) #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Machine Learning in Materials Science #Meta-analysis and systematic reviews #scientometrics and bibliometrics research
paper · pdf · doi:10.48550/arxiv.2105.08089
openalex publication_date 2021/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Researchers are often evaluated by citation-based metrics. Such metrics can inform hiring, promotion, and funding decisions. Concerns have been expressed that popular citation-based metrics incentivize researchers to maximize the production of publications. Such incentives may not be optimal for scientific progress. Here we present a citation-based measure that rewards both productivity and taste: the researcher's ability to focus on impactful contributions. The presented measure, CAP, balances the impact of publications and their quantity, thus incentivizing researchers to consider whether a publication is a useful addition to the literature. CAP is simple, interpretable, and parameter-free. We analyze the characteristics of CAP for highly-cited researchers in biology, computer science, economics, and physics, using a corpus of millions of publications and hundreds of millions of citations with yearly temporal granularity. CAP produces qualitatively plausible outcomes and has a number of advantages over prior metrics. Results can be explored at https://cap-measure.org/