Not much of a sample size, although I’ll bet the pattern holds true.
Waiting, patiently, in a non-gendered way, for the first comment along these lines: “Yeah but that’s a dumb thing to study. Ur dumb and u study dumby things.”
So Cory, when you began each of your books what influenced the gender of your protagonist? Is it a deliberate decision and will you change to more non male protagonists?
I will buy, read and cherish anyway.
How does this distribution compare to the distribution of novels that were eligible for these awards?
Were you presenting your question as a counterpoint, or as a supporting point?
If you had read just the summary, not even the linked article, you would see that this project intends to, but has not yet, gotten an answer to your question.
This project is literally in its first week of existence. Expecting comprehensive results from them is not reasonable. But they aim, over time, to study gender in novels:
- submitted to publishers
- accepted for publication
- really promoted by publishers
- long-listed for awards
- short-listed for awards
- winning awards.
Indeed. Fifteen data points divided into seven categories makes it hard to show anything meaningful. Opening it up to Hugo Best Novel nominees would be interesting (if only because Embassytown would put one in the “by men about women” bucket).
But the project is already a week old! It’s pretty far-fetched to expect them to expand much beyond this one pie-chart. Perhaps in a few years they might change the color-scheme…
Considering that books by men are reviewed far more often than books by women… I’m going to go with “compares really well”.
As always, the Vida Counts are good and enraging.
Neither I suppose. Without knowing the underlying distribution we can’t really say whether or not the numbers are high or low.
Yes, that’s just basic stats. You need to define your population, your sampling method, the size of the sample before you can even examine your data, never mind draw your conclusions.
And if you want to be honestly rigorous and avoid data dredging and the green jelly bean problem, you define what you’ll be testing up-front so that you don’t just latch on to a spurious correlation that pops up.
Of course, none of this applies if you’re a politician. Then you define the conclusion you want ahead of time, and keep on collecting data until you find some that supports it.
Meaningless without knowing how many books are being written each year in each of these categories.
We’re probably going to need another thread on the gender split of people producing competent statistical analyses…
And we’re going to need a hell of a lot more categories to cover all the new genders people are coming up with.
and we’re gonna need a bigger boat…
This topic was automatically closed after 5 days. New replies are no longer allowed.