I woke up late because I was working late on things from the mundane physical world, which means ploughing through spreadsheets and making columns think they are rows and rows think they are columns. This meant this morning I had less time for my entertainment, which means ploughing through spreadsheets and making columns think they are rows and rows think they are columns. Specifically, I wanted to wrangle the IGNYTE award finalists into a spreadsheet with a similar format to the one I’m using to collate Dragon Award finalists.
One bonus for the IGNYTE awards is the finalist list typically includes publisher where it is relevant. So that’s good. A downside (purely from a tabulating things perspective) is some of the names attached to a single finalist are as many as 20+ but that is a good challenge. Often things that look convenient from a data perspective (‘a book’ has ‘an author’) reveal assumptions about our world. Writing a list of stuff is something imbued with socio-political perspectives that can literally trip you up. Counting people means categorising people and categorising people is not a simple thing.
A key issue is counting gender. There are good reasons for tabulating gender because it is an almost universal issue of social disparity across the world. We can see a lot about social change and inequality by looking first at gender and we have roughly two major categories of people (male, female) plus some proportionally smaller ones. But gender is also complex and I’m not sure about the best way of counting it. Also, even though I primarily present gender stats in aggregate, I still am going through lists of authors and sticking them in a gender box.
Looking at the ‘how’ of the classifications I decided to modify the way I was tabulating gender. What I did with the Dragons was check Wikipedia entries or other sources of author bios and looking for indications of gender…but really what I was doing was checking pronoun usage. So, if what I was actually doing was counting pronoun usage then a smarter move was to tabulate PRONOUNS rather than gender i.e. instead of ‘other/non-binary’, ‘male’, ‘female’, use the categories of ‘they/other’, ‘he’, ‘she’. That way the presentation of the data matches what I was actually doing.
I don’t know if that’s the best approach but it has another benefit to a different question about the gender of authors. In note  below, the issue of James S A Corey was pertinent – two people who author books under a single name. Daniel Abraham and Ty Franck are both men but what gender is James S A Corey? It’s not just an abstruse philosophical question because if we are thinking about how sexism plays out in awards or book purchases etc the presentation of gender is relevant. “Robert Galbraith” is a pseudonym used by author J.K.Rowling and at least initially it was a secret that Galbraith was Rowling, which presents an interesting question when classifying books by the gender of their author.
Historically this has a further layer. Many women have used male pseudonyms (or made their gender less obvious by using initials) as a way to avoid sexism in book purchasing etc. However, some authors in the past who used pseudonyms of a different gender to their ‘everyday’ names did so for other reasons i.e. as a means for exploring their own gender. Nor are those mutually exclusive motivations. Authors regarded socially as female may have chosen male pseudonyms both to avoid sexism and to express their own understanding of their gender, nor is it going to be entirely clear which.
There’s a sort of moral to this story which is the unsurprising conclusion that gender is complex. The specifics in this case is that classifying authors by gender is complex REGARDLESS of your views on gender. You could have quite regressive views on gender (e.g. J.K.Rowling) but that doesn’t change that there are cases of authors were gender can be hard to classify (e.g. Robert Galbraith).
e.g. back in 2019 some people scoffed that I’d made an error saying 10 men had won Dragon Awards in the two headline categories because four years and two categories comes to 8, so how could it be 10 etc. https://camestrosfelapton.wordpress.com/2019/09/05/a-bit-more-on-dragons-and-probabilities-etc/
 A reminded that ‘proportionally small’ can add up to a lot of actual people https://camestrosfelapton.wordpress.com/2020/07/05/i-love-manchester-but-i-must-destroy-it/