Our results show that women’s contributions tend to be accepted more often than men’s [when their gender is hidden]. However, when a woman’s gender is identifiable, they are rejected more often. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.
The study differentiates between male and female only and purely based on physical features such as eye brows, mustache etc.
I agree you can’t see one’s gender but I would say for the study this can be ignored. If you want to measure a bias (‘women code better/worse than men’), it only matters what people believe to see. So if a person looks rather male than female for a majority of GitHub users, it can be counted as male in the statistics. Even if they have the opposite sex, are non-binary or indentify as something else, it shouldn’t impact one’s bias.
