| gender-package {gender} | R Documentation |
Gender: predict gender by name from historical data
Description
Gender: predict gender from names using historical data
Details
Encodes gender based on names and dates of birth, using U.S. Census or Social
Security data sets. Requires separate download of datasets, which should be
done automatically and can be done manually by running
install_genderdata_package().
This package attempts to infer gender (or more precisely, sex assigned at birth) based on first names using historical data, typically data that was gathered by the state. This method has many limitations, and before you use this package be sure to take into account the following guidelines.
(1) Your analysis and the way you report it should take into account the limitations of this method, which include its reliance of data created by the state and its inability to see beyond the state-imposed gender binary. At a minimum, be sure to read our article explaining the limitations of this method, as well as the review article that is critical of this sort of methodology, both cited below.
(2) Do not use this package to study individuals: it is at most useful for studying populations in the aggregate.
(3) Resort to this method only when the alternative is not a more nuanced and justifiable approach to studying gender, but where the alternative is not studying gender at all. For instance, for many historical sources this approach might be the only way to get a sense of the sex ratios in a population. But ask whether you really need to use this method, whether you are using it responsibly, or whether you could use a better approach instead.
Blevins, Cameron, and Lincoln A. Mullen, “Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction,” *Digital Humanities Quarterly* 9, no. 3 (2015). http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html
Mihaljević, Helena, Marco Tullney, Lucía Santamaría, and Christian Steinfeldt. “Reflections on Gender Analyses of Bibliographic Corpora.” *Frontiers in Big Data* 2 (August 28, 2019): 29. https://doi.org/10.3389/fdata.2019.00029.