Last Place: The Intersection between Ethnicity, Gender, and Race in Biomedical Authorship
We investigate how ethnicity, gender, and race are related to the probability of being last author on MEDLINE articles (In biomedical science, last author runs the lab and/or is the principal investigator, which is an indicator of career independence). We combine MEDLINE publication data with three additional databases and use machine learning to clean, combine, and impute a range of information. In addition to studying the relationship between last author position, ethnicity, gender, and race, we leverage the massive size of our data to highlight the importance of intersectionality, the idea that ethnicity, gender, and race are not necessarily additive, but interact to determine experiences and outcomes. This analysis is timely because of serious concerns with underrepresentation of women and minorities in biomedicine and other STEM fields.