Lauren Aragones
Pronouns: she/her
Research Mentor(s): Jinseok Kim
Research Mentor School/College/Department: Institute for Social Research / Information
Program: UROPF
Session: Session 3 (11:00am – 11:50am)
Authors: Lauren Aragones, Jinseok Kim
Abstract
The Gender Productivity Gap†narrative claims female scholars are less productive and therefore less impactful than their male counterparts. This narrative is potentially based on data where all of a researchers works are not properly attributed to them because of variations in the publishing name. Female authors are more likely to be incorrectly identified than males since many often use maiden names in publication prior to marriage, then alter their names after. The main conclusion of this project will be determining whether the gender productivity gap narrative is supported or negated by the disambiguation of female authors. Scholar profiles detailing name changes of female authors in the fields of computer science, nursing, and communications are collected from archival sources. This data is then cleaned and compared among researchers to search for discrepancies in analysis. It is then used to train machine learning algorithms to identify name changes and correct attributions for female authors. By analyzing the data where female author names are accurately distinguished by these algorithms, female academic productivity can then be more accurately compared to that of males. It can then be determined if females in academia are truly more unproductive or if this previous conclusion is based on flawed data. The machine-learning-based disambiguation method can then be used to properly disambiguate authors in a large-scale analysis of bibliographic data. Most directly, this research is beneficial to female science authors by accurately giving the credit for their works. Beyond female authors, this research could also be beneficial to all women in academia beyond science authorship because it could lead to the more proper recognition of women’s contributions to the overall academic conversation. It could also prompt further research into the implications and limitations of claims of gender productivity gaps in other fields and contexts.
