SF writer Nicola Griffith reports in from her Literary Prize Data, which is collating data on gender and genre awards (and showing a dismally predictable skew towards books by and about men and boys).
At this stage I'm less interested in the Why than the What and the How Many. Why, in my opinion, can only emerge when we dig deeper and get a clear picture of what's actually happening (and manage to look past our biases–we all have biases). That will take time. We need surveys of writers' organisations and ask: When you began your book, what influenced the gender of your protagonist? And then ask agents how they chose the books to represent. And then publishers what numbers of books about women and about men were submitted, accepted, supported etc. Which were submitted for review, and where. Which were praised, and by whom. Which were put on new fiction tables at the front of bookstores and libraries. Which submitted to prizes, and why. Which were long-listed, then short-listed, then chosen for the prize. Then remembered.5
But it has to start somewhere. And that's what I've done. I started Literary Prize Data6, a group to count, share, collate, present, and discuss book numbers. Right now we number about 35 from three continents.7
The group is new: one week old. But already we have people working on the Edgars, the Campbell, taking a more granular look at the Hugos, and more. Some of us are genuine data geeks. Some novelists. Some academic researchers. Some readers. We could use all the help we can get. If you want to help, sign up. Count something. Help design the best way to interpret and present what you and others have counted. Actually counting, and then finding different ways to parse the results, and different ways to display those results, makes the reality more concrete than ever. If we're transparent about what we're counting and how, the conclusion—that not only more men than women win prizes, but that even the women who win are likely to win for writing about men—is difficult to argue.
Data, books, and bias [Nicola Griffith/Charlie's Diary]
