Read “Fighting Demons,” the sequel to “Hunting Monsters,” today at The Book Smugglers!

What is this story about?  It’s about parents and children, culture and identity, individuality and family.  It’s about the daughter of Beauty and Red Riding Hood meeting the son of Bai Suzhen and Xu Xian from the famous Chinese tale “Legend of the White Snake.”

If you want to read about how awful and hard it was to write about my own cultural heritage for this story, by the way, I wrote about it here:

Doubts plagued me:

How do I write for primarily non-Chinese readers without flattening all of Chinese culture into a shallow, singular narrative?

What if I write something that only reinforces stereotypes, despite my best efforts?

Because I’m mixed, because I’m diaspora, do I even have the right?

Looking back, however… part of what made it so hard is also what makes me most proud of it.

But I’ll let that essay stand on its own.  Instead I’ll talk for a minute here about a bit of other background — setting!

“Fighting Demons” takes place primarily near a fairy tale version of Hangzhou, China.  Fun fact: I lived in Hangzhou briefly one summer, teaching a computer science class to electrical engineering graduate students at Zhejiang University.  The Thunder Peak Pagoda referenced in the story is a real place, and one I have been to!  I drew on all my sense memory writing this story: the humidity that fills you up and wraps you close and heavy like a blanket; the vast and scenic West Lake, its shallows forested with lotus; the pagoda spiking up above us as we walked on the lake’s shores.

The real Hangzhou is very different from Fairy Tale Hangzhou, of course.  There are no sea serpents, nor magic defenses in the pagoda, nor snake demons fighting battles across the lake.  The West Lake retains its beauty while the present-day city is as modern and vibrant as any other.  Zhejiang University itself is ranked sixth in all of China.  My students were all bilingual and spoke excellent English (well, some of the main characters in “Fighting Demons” do, too), and were sharp, smart, and politically astute about the modern world, most far more than I was.  (“Don’t bring up Tiananmen Square, Falun Gong, or Tibet,” they warned us before we went to China.  They didn’t tell us what to do if our students asked us our opinions on exactly those topics, seriously and pointedly.)

But even though my fairy tale Hangzhou differs from the real, modern one, I hope I’ve given it the same depth and reality I gave to fairy tale Europe in “Hunting Monsters.”  After all, if I’ve only learned one thing from all the places I’ve been, it’s that people are people no matter where they live, and families are families.  Even — especially! — families made up of immortal snake demons who fight obsessive monks to rescue each other with magic.

Enjoy the story!

HOLY CATS FOLKS LOOK HOW AWESOME THAT COVER IS!  It is my FAVORITE COVER YET.  Credit as always is to my wonderful cover designer, Najla Qamber.

My first readers tell me this is also the best book in the series yet.  Feedback included “SOOO EXCIITINGSAUUCE” and “WHEN IS NEXT BOOK?”  To answer, next book is done and already through its first round of beta, and will likely release in spring of 2016. \o/

So what is this excitingsauce book about?  Here is the blurb!

 

Cas Russell has always used her superpowered mathematical skills to dodge snipers or take down enemies. Oh, yeah, and make as much money as possible on whatever unsavory gigs people will hire her for. But then one of her few friends asks a favor: help him track down a stolen math proof. One that, in the wrong hands, could crumble encryption protocols worldwide and utterly collapse global commerce.

Cas is immediately ducking car bombs and men with AKs — this is the type of math people are willing to kill for, and the U.S. government wants it as much as the bad guys do. But all that pales compared to what Cas learns from delving into the proof. Because the more she works on the case, the more she realizes something is very, very wrong . . . with her.

For the first time, Cas questions her own bizarre mathematical abilities. How far they reach. How they tie into the pieces of herself that are broken — or missing.

How the new proof might knit her brain back together . . . while making her more powerful than she’s ever imagined.

Desperate to fix her fractured self, Cas dives into the tangled layers of higher mathematics, frantic for numerical power that might not even be possible — and willing to do anything, betray anyone, to get it.

 

Root of Unity is the third installment in the Russell’s Attic series, following Zero Sum Game and Half Life.  Do you need to have read the first two books to enjoy this one?  Probably not, but it helps.  The plot here stands alone pretty well, but the relationship arcs have been building since book 1 and there are references to things that happened in books 1 and 2.

PREORDER ROOT OF UNITY NOW!  ON AMAZON OR ON AMAZON UK!  It will have a 9/30 release date on all the non-Amazon places, too — preorder links from Apple and Kobo coming soon.

---

 

OH HEY do you want more Russell’s Attic content without having to wait?  I’ve written a new novelette called “Ladies’ Day Out,” in which Cas teaches Pilar to shoot and hilarity ensues.  It takes place between books 2 and 3 but can be read before or after book 3.  It’s free for mailing list subscribers and will be available at retailers September 1.

• Want to read it now?  Join my mailing list to get the password, and then click here!
• Rather buy it for 99 cents?  Preorder it from Amazon, Amazon UK, or Kobo (more retailers coming soon).

Sometimes I see a “top X” list that’s, shall we say, all male (think lists of top scientists, recommended SFF authors, etc.).  And when people object, others defend against the objection with, “But what if the field’s mostly male???”

There are a whole host of problems with this, but I’m not really going to get into them here.  I’m just going to do some math.

As far as I can figure, my starting assumptions are only that (1) we expect Top-X lists to sample gender randomly — that is, that a male person in the field is not automatically expected to be better than a non-male person already in the field, and (2) there is no institutional sexism going on beyond whatever might cause the gender skew in the first place.

If we see unlikely Top-X lists, one of these assumptions must be wrong.

Let’s look at a Top-10 list.

The Approximate Probability of an All-Male Top-10 List

If a field is 50% male, the likelihood that a Top-10 list will be entirely male is .098%.

If a field is 60% male, the likelihood that a Top-10 list will be entirely male is .60%.

If a field is 70% male, the likelihood that a Top-10 list will be entirely male is 2.8%.

If a field is 75% male, the likelihood that a Top-10 list will be entirely male is 5.6%.

If a field is 80% male, the likelihood that a Top-10 list will be entirely male is 11%.

If a field is 90% male, the likelihood that a Top-10 list will be entirely male is 35%.

I note that even in the most extreme case — 90% male is a VERY extreme gender skew — only about 1/3 of Top 10 lists would be expected to be composed entirely of men.

This math is very easy, by the way, and you can replicate it quickly for any Top-X list and any percentage of men.  If m is the percentage of men written as a decimal, just raise m to the power of X.  So to find the likelihood a Top-25 list is all-male if you suspect a field of being 3/4 male, you would do (.75)^25 (which incidentally equals .075% — in other words, it’s EXTREMELY unlikely for even a field that is 3/4 male to have a Top-25 list that is all-male).

Whether any skewed percentages are a result of other biases in the first place is, of course, another discussion.  But if you find yourself with extremely probabilistically unlikely Top-X lists even given skewed percentages, then maybe it’s worth thinking about why that might be.  And if the “you” in question is a magazine, bookstore display rack, publisher’s promo list, other Official Book Industry Recommendation List, review blog, fanzine, etc. . . . it might be worth urging your staff to be somewhat less unlikely.

Math note: I’ve sampled with replacement here, on the assumption that the field is big enough relative to X that removing up to X people for the list has not changed the gender ratio among the population of people not on the list. If you have a (relatively) small field or a large list, the math becomes more complicated.

Comments are open, but I may not have time at the moment to respond (I still haven’t caught up on the comments for my LAST gender and math post, argh I am the worst!). Comments will still be moderated if necessary — please be kind to each other.

eta: Even though I triple-checked, I made a copy-paste error — the 50% line initially read .0098% instead of .098%. SORRY!

Due to Enormous Real Life Events, I’m still way behind on the comments on my last Hugo post (ARGH! SORRY!), but, you know, if I made myself catch up on everything ever before saying anything new, my inbox would be empty but I wouldn’t have a writing career.

So, this Best Saga Hugo.

I instantly loved the idea, so I probed on Twitter trying to figure out why I was seeing so much negativity about it.  Some people said they supported it in theory but the devil was in the details of this proposal (which, I believe, is still in its revision stage, so I hope it gets thrashed out to people’s satisfaction (eta: revisions being discussed here!)).

But others brought up the same arguments I’d seen elsewhere: that we shouldn’t have this award because it would favor established and/or white male authors.

Now, a lot of people know both the Hugos and the field a lot better than I do.  For instance, when I first saw the suggestion, one of the ways it immediately appealed to me was that it felt like it would lead to a greater likelihood of urban fantasy and SFR nominations, two subgenres that are historically ignored (and have a lot of female authors writing them).  Twitter, unfortunately, told me I was naive about this, because girl cooties.  I admit Twitter does know more than I do about these things, just as lots of other people in the field know way more about the history of the genre and the awards than I do.^[1]

So although I like the idea of Best Saga because it matches the way I read — I love series, and there are series I would nominate as a Saga at the drop of a hat while not feeling any installment deserves Best Novel — I don’t feel I quite have the authority to speak to whether such a category change would be advisable for the Hugos or beneficial for the field.  My instinct is that a demographic argument is not really a good one — and I’m honestly still confused as to why we wouldn’t want to honor established authors whose work may be hugely loved but doesn’t fit neatly into other categories — but on the whole I’ll leave those larger arguments to more knowledgeable heads than mine.

But considering that so much of the conversation is centered on this award favoring established, white, male authors, as an unestablished, POC, female author — and one whose first work has been part of a series — I feel I do have something to add from that perspective.

Which is: more awards mean more people talking about their favorite things.  And now that the axing of Novelette is no longer part of the proposal, the addition of another category means more people talking about more of their favorite things, which can be good for lots of people other than established white men.^[2]

To demonstrate, let me tell you my experience as a person who was nominated for zero awards this year. Because there’s this thing that happens around awards season, which is that people start recommending stuff, and it’s pretty heady.  So here’s what happened to me:

• Best Short Story is a category.  My eligible short story was on more than one person’s “best of” list. People were talking about it and reading it.  I didn’t get a nomination, but being talked about — AMAZING,  YAY!
• The Campbell is a (not a Hugo) award for new writers.  I’m a new writer.  I saw myself get recommended . . . probably ten or twelve times.  Let me tell ya, could’ve knocked me over with a feather the first time it happened!  But the point is, if the Campbell Award weren’t a thing that exists, then people would not have been mentioning my name for it.
• Best Novel is a category.  I don’t think I got a whole lot of votes for this — I think most people who like my novels thought of me for the Campbell (YAY! FEATHER! THANK YOU!) but I was mentioned a couple times, including in discussions of diversity in novel lists.  For example, when Kameron Hurley asked come awards season for people’s favorite 2014 novel by a woman of color, and HEY LOOK PEOPLE ARE MENTIONING ME.

In other words:

• Zero women of color were nominated for Best Short Story this year, but the existence of the award still benefited me, a woman of color.
• Zero women of color were nominated for the Campbell this year, but the existence of the award still benefited me, a woman of color.
• Zero women of color were nominated for Best Novel this year, but the existence of the award still benefited me, a woman of color.

As a completely unestablished, non-white, non-male person writing a “Saga,” I admit it kinda makes my teeth itch that people are telling me the existence of this award would be bad for people like me.  Yes, I’m completely unlikely to be nominated for such a thing in the foreseeable future.  But hey, you want to know what would happen if we had a Best Saga award?

People who like my books would talk about them more.

People who are interested in making sure their reading and awards nominations don’t only privilege white men would start asking around for women and POC writing sagas, and my books would get talked about more.

I like being talked about more.  And I, as an unestablished person, might even get more just out of being talked about than the Already Very Popular and Established Author who actually wins the award.  (But if he gets a bump too, who cares?  I refuse to believe publishing is a zero sum game.  The category benefiting someone else more doesn’t mean it still wouldn’t benefit people like me.)

And yeah — I said this in one of the footnotes, but it bears repeating — I get why people might think Best Saga is less valuable than an award category that might have a broader demographic bent, like Novelette or YA.  And if the proposal were coupled with one of those still, I’d understand people pushing against it for those reasons.  But now that it’s not, there’s no zero sum game here, either, is there?

Honestly, I’ve been trying to figure out why I feel so bothered by the above sorts of arguments, and I think it feels a little like the existence of POC and women writing series is being erased or ignored.  Which is . . . not a very nice feeling, you know?  Because we’re here, and we’re doing it.  Instead of completely shutting down the category that best fits our work, wouldn’t a better solution be to have the category . . . and then recommend the heck out of your favorite women / POC / more-unknown authors who are writing in it?  Rather than saying we don’t want a category unless it’s already more evenly demographically split, why not use the existence of the category to give word of mouth to your favorites who are overlooked in that form?  To help make the form more fair by getting people to read your favorites?  After all, the more they sell, the more publishers will support other series like them . . .

Again, maybe I’m naive.  Maybe there are bigger sociological arguments I’m missing.  I’ll freely admit there may be.

But I felt like I should share this perspective.  Because I’m the exact category of person these arguments are supposed to be defending, and I just . . . don’t feel very defended by them.

 

Closing comments because I am WAY behind on everything right now and I don’t have time to moderate at the moment (and I don’t want the thread to accidentally run away).  But please feel free to approach me on Twitter — @sl_huang — and tell me if I’ve got the wrong end of the stick somehow.  If convinced, I’ll happily edit this post.

I’ve been traveling without a laptop and have been terribly remiss on updating the Interwebz!  (Still traveling, but laptop is back so hopefully I can catch up a little.)  Anyway, I HAVE A NEW SHORT STORY!  And my essay is in a NEW CRITICAL ANTHOLOGY!

• “By Degrees and Dilatory Time” was published last week at Strange Horizons, along with beautiful illustrations (eek!) and an audio version (eek again!).  It’s a story that’s very personal to me and one I’m very proud of.  Many, many thanks to my generous betas for making the story better, the excellent editors at Strange Horizons for making it even more better, and the incredible illustrator and narrator.  You all improved this story by a factor of a thousand!
• I’m very proud to say my essay “Nobody’s Sidekick: Intersectionality in Protagonists” is part of the anthology of essays on representation Invisible 2, edited by Jim C. Hines.  I hopped on the Amazon page on release day (was going to try to tweet it but failed at phone copy/paste), and we hit at least #2 in the SFF History & Criticism category.  Whee!  (Also, all proceeds go to Carl Brandon Society to Con or Bust.)

Check ’em out!

I’ve been trying to stay out of saying anything about the Hugos Awards, mostly because lots of people are saying lots of things already and I haven’t felt like I have anything to add.  But then Jim Hines posted today speculating about MATH, and, well, I got nerd-sniped.

Here’s the original (long, long) comment I left on his blog.  I finally decided I couldn’t not do a normal distribution and standard dev, so I came back here for it, but the numbers in the original comment might be a bit more intuitive for non-math folk than what I’m going to do here.

Motivation

The Hugo Awards are a SFF award nominated by popular vote.  There is some controversy (understatement) about the nominations this year.  I’m not going to get into that here, just going to display some numbers.

It would, however, be disingenuous not to state my own bias, which is that I think institutional discrimination against women and people off the gender binary exists and is a problem.  I’ve allowed that bias to affect how I frame my wording (and I’ve editorialized at times), but I’ve performed the math exactly as I believe is correct.  Since it’s very possible to make statistics seem skewed toward a particular viewpoint by bad-faith numerical sleight of hand, I want to state up front that I have not done so here — any poor mathematics or misunderstanding of confidence levels is due to (1) my lack of background in stats or (2) genuine error.

What I’m doing, and what it means

The four writing categories for the Hugo Awards have 5 nomination slots each, for a total of 20 nominations for fiction writing.  I’m going to make the probability distribution for the likelihood of a particular gender split (e.g., find the probabilities of a 10/10 split, or a 9/11 split, or a 15/5 split, etc).  This will approximate a nice normal distribution.  If you don’t know what that is, that’s okay — the important part is the next bit.

Once I have the probability distribution, I’m going to take the standard deviation.  Standard deviation is a very useful statistical tool that tells us the likelihood something will be in a given range of numbers.  For example, it’s not terribly useful to look at the probability of a exact 8/12 split — it’s more useful to look at the probability the gender split will be within a certain range of numbers.

For a normal distribution, 68% of the data will fall within 1 standard deviation of the mean (the mean = the average), 95% will fall within 2 standard deviations of the mean, and almost 100% will fall within 3 standard deviations of the mean (99.7%).  Once we get out to three standard deviations from the mean, we’re talking about extreme outliers.

This will tell us whether a given gender distribution is within what we’d consider an expected year-by-year fluctuation from 50/50, or whether, assuming a 50/50 gender split, it would be…well, an extreme outlier.

Caveats

• I’m a mathematician but NOT a statistician; I’ve never actually studied stats.  I only know enough basics to get me in trouble.  If you know more stats than I do, please jump in!
• I’m considering gender to be 50/50 split on a male/female binary because I couldn’t quickly find stats on nonbinary folk.  (Sorry!!)
• I’m the type of mathematician who hasn’t worked with numbers in so long that I’m very prone to arithmetic mistakes.  If you find any, please shout.

Tools

• This calculator, because I’m lazy: http://graphpad.com/quickcalcs/probability2
• This graphing tool, to make pretty pictures: http://www.wolframalpha.com

The Data

I’m keeping it easy: 20 nomination slots, 50% probability of a given gender getting a nomination.^[1]

I haven’t talked about much specific Hugo data here, but when I have I’ve pulled it from the graph in Jim Hines’ post.

Binomial Probability and the Frequency Distribution

Binomial probability gives us the following distribution — conveniently, the calculator above gave it to me all in one go when I entered n=20 (20 nomination slots) and p=.5 (50% probability of male or female).  The following table is copy/pasted verbatim from the results.  For non-math people, note that we’re not calling a male person or a female person in a nomination slot a “success” or a “failure” in the semantic sense — here “success” and “failure” are neutral probability terms.

Binomial, Poisson and Gaussian distributions

Number of trials (or subjects) per experiment: 20
Probability of “success” in each trial or subject: 0.500

Number of Successes	Number of Failures	Exact Probability	Cumulative Probability
0	20	0.000%	0.000%
1	19	0.002%	0.002%
2	18	0.018%	0.020%
3	17	0.109%	0.129%
4	16	0.462%	0.591%
5	15	1.479%	2.069%
6	14	3.696%	5.766%
7	13	7.393%	13.159%
8	12	12.013%	25.172%
9	11	16.018%	41.190%
10	10	17.620%	58.810%
11	9	16.018%	74.828%
12	8	12.013%	86.841%
13	7	7.393%	94.234%
14	6	3.696%	97.931%
15	5	1.479%	99.409%
16	4	0.462%	99.871%
17	3	0.109%	99.980%
18	2	0.018%	99.998%
19	1	0.002%	100.000%
20	0	0.000%	100.000%

 

Cool!  This gives us a frequency distribution.

The Normal Distribution

Binomial probability (what we just used to get the frequency distribution in the above table) with p=.5 and a reasonable number of data points is known to approximate a normal distribution, aka a bell curve.  Here’s a normal distribution via Wolfram Alpha of these data:

Notice that it’s centered around the mean (average) of 10, as we would expect.  We’ve got the number of nominees of a given gender on the x-axis (it doesn’t matter which gender we choose, as it’s symmetric — we could say the x-axis is the number of male nominees or we could say it’s the number of female nominees), and the percent probability we’ll land on that number of nominees on the y-axis.

Whether we look at the table or the graph, we’re hitting about a 17-18% probability of an even 10/10 split, and it drops off quickly on either side, until a 0/20 split in either direction has almost a 0% probability.

Standard deviation

(I actually found the standard dev first and used that to graph the normal curve, but shhh!  I think it’ll make more sense to non-math people to write it in this order.)

One reason it’s so lovely to talk about standard deviations in a normal distribution is it gives us very pretty ranges that other people who know basic stats can easily grasp — if you say “more than a standard deviation from the mean,” people who know what standard deviation is will have an idea of how hefty a divergence that is.  Here’s a great visualization for standard deviation on a normal distribution:

As you can see, the dark blue is within 1 standard deviation of the mean and takes up 68.2% of the data.  The lighter blue shows going out another standard deviation from the mean, and the even lighter blue goes out to a third standard deviation from the mean, where the probability of landing is very close to zero.

Standard deviation has a complicated formula that’s beyond the scope of this post — I just used my calculator.  The standard deviation for these data is about 2.236.

For a normal distribution, that means 68% of the data fall within 2.236 of the mean.  In other words, 68% of the data fall within a difference of 2.236 from 10, or between 7.764 and 12.236.

It’s easy to check that this is about right: if we go to our table above and add the “exact probability” column for 8, 9, 10, 11, and 12, we get a bit above 70%.  It’s not exact because our frequency distribution is only approximating the normal distribution, but it’s a very good approximation, and it’s generally considered an appropriate model for binomial distributions with non-extreme probabilities and a reasonable number of trials.^[2]

One Standard Deviation, Two!  Three Standard Deviations, More!

Remember that about 68% of the data will fall within 1 standard deviation of the mean, 95% will fall within 2, and 99.7% will fall within 3.  In other words, another advantage of standard deviation is that it gives us some nice arithmetical shortcuts, as follows:^[3]

• 1 standard deviation:  7.764 – 12.236
• OR: About 68% of the time, the gender split will be 8/12 or closer.

• 2 standard deviations: 5.528 – 14.472
• OR: About 95% of the time, the gender split will be 6/14 or closer.

• 3 standard deviations: 3.292 – 16.708
• OR: About 99.7% of the time, the gender split will be 4/16 or closer.

And finally:

• A gender split wider than 4/16 is an extreme outlier.^[4]

Note that though a split wider than 4/16 suggests something very statistically unlikely is going on, it does not say why, and it does not assign intent.  My lived experience suggests that intentional sexism should not generally be assumed when systemic bias will suffice, and in a process like writing, publishing, publicity, and awards nominations, there are plenty of stages at which institutional bias can manifest itself.  This does not, of course, mean there is not a problem — in fact, it would mean the problem may be one that requires more thought, awareness, and effort to address.

I’ll further note that if you consider the years 2010-2014 (none of which had fewer than 7 nominations for either gender) and compare them to 2015,^[5] and this leads you to conclude (along with a preponderance of other data, I am aware) that something untoward happened in 2015, I’ll further note that even one person or one small group of people with a particular subgenre taste having chosen a fantastically statistically unlikely slant of genders still does not imply malicious sexism.^[6]  What it does imply, in my opinion, is a variety of other extremely upsetting problems, exacerbated by the fact that nonmalicious sexism can be much, much harder to combat.

So.  What was the gender split in the writing categories is this year?

• 3/17.