Is there any gender/race bias in hep-lat primary publication? Machine-Learning Evaluation of Author Ethnicity and Gender
May 16, 2022
In this work, we analyze papers that are classified as primary hep-lat to study whether there is any race or gender bias in the journal-publication process.
We implement machine learning to predict the race and gender of authors based on their names and look for measurable differences between publication outcomes based on author classification.
We would like to invite discussion on how journals can make improvements in their editorial process and how institutions or grant offices should account for these publication differences in gender and race.
How to cite
Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating
very compact bibliographies which can be beneficial to authors and
readers, and in "proceeding" format
which is more detailed and complete.