Cleaning and visualising data from the Gender Census' 2024 responses to try and gain more nuanced insights into the gender diverse community represented in this data set.
(Mostly LGBT+ related) slurs are used in the identity label section, including in the cleaned q2 file names sorting those labels.
The label write ins also includes controversial words like transsexual and hermaphrodite, and a LOT of MOGAI microlabels.
I want us to better understand the composition of the community currently responding to this survey, both to get a better understanding of them (f.e. how many of them are trans? what's the rest's deal? how does (other) queerness or neurodiversity factor into it? how do pronouns/titles/etc intersect with identity words? how many respondants align themselves with one binary over the other? etc) and to be able to address any obvious, overlooked inequalities in the future (f.e. we know from USTS data that people who identify as nonbinary tend to have a 80% afab/20% amab ratio, which has not notably budged between 2015 and 2022. Even though OP refuses to track birthsex, we can still get some birthsex data from the labels, and may be able to confirm amab gender diverse people's lack of representation here too and attempt to address that lack in the future via better outreach work (which we can get data for from the "where did you find this survey" responses).)
