Kristin Antelman

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About

Kristin Antelman is a leading researcher in scholarly communication and open access (OA) infrastructure, best known for her critical work on the accuracy and reliability of automated OA detection. Her most-cited study, a 2005 signal-detection analysis of algorithm performance in identifying OA articles, revealed a significant tendency for robots to overcode for OA—finding that in a Biology sample, 40% of identified OA articles were false positives. This foundational contribution has shaped how the research community evaluates and trusts automated metadata, directly influencing the development of more rigorous OA classification methods. With over 3 citations on this pivotal paper alone, Antelman’s work has had a lasting impact on the integrity of open access discovery systems. Her research bridges library science, data analysis, and scholarly publishing, offering essential insights for students and researchers navigating the complexities of OA metrics. By exposing the limitations of early automation, Antelman helped pave the way for more transparent, human-verified approaches to open access identification, cementing her role as a critical voice in the evolution of digital scholarship.