Chapter 13: Potty Problems

When action grows unprofitable, gather information; when information grows unprofitable, sleep.

— Ursula LeGuin

If you take this wrong, fuck you.

No, really. This isn’t any one character’s position, or even mine, whoever I am — and I’m not sure who I am, this person who flies ahead like Heathcliff to open the gate, who runs behind like an out-of-shape footman, anxiously waving a coat in the character’s wake.

This is an absolute moral position.

I’m not sure what’s going to happen with Serena’s mute struggle against the forces that want to harass and eliminate Potential Problem Pissers, but if you haven’t been elderly and incontinent and really needing to keep a job — or someone with Crohn’s disease — or a woman beset by the kind of heavy flow that means you get to the bathroom and change and you make it back to your workstation only to find that in the short walk you’ve drenched what should be an overnight pad, so you turn immediately around and walk back to the bathroom, under the baleful eye of your supervisor — or a mother who pumps milk in a filthy stall during your solo night shift at a convenience store — or a dad whose kids are at home alone and have urgent concerns you need to answer, by text message, while you hunker over the urinal — or someone whose only option is a foul Port-o-Potty or an open ditch — then fuck right the hell off and stay fucked off.

But beyond that, what can one say? Serena opened up the databases, when she had time, and poked around. There were lists of current restroom lengths based on key ins/key outs, but the company was trying to be super-nice to anyone with an active problem. They just wanted to root out potential problems … from people who hadn’t yet become a problem, and from people they might hire. So Serena, with no higher title than “Human Resources Assistant,” had to create an predictive algorithm for problem pissers.

First she looked at the data carefully. Like most self-taught users of databases, she had come up through a six-month secretarial course and had learned to code on the job, and despite (because?) of that could commune with them better than most college-taught DB architects. She could program in SQL, was expected to troubleshoot tables and scrub data on her “down time” between running rules updates and materials safety data sheets around the plant and carrying verbal messages to people who “needed to come to HR.” She made $12 an hour.

She is sitting in her little half-walled cubicle in the HR department. Gray light slants through the tall windows opposite. It’s May, and raining again. The space is beautiful; the workstations of the junior staff, abysmal. Serena can just sit, in her awkward too-tippy chair, with one elbow jammed against an elegant, plum-colored partition wall and her head craned down to see the laptop screen. She has to turn in the laptop every day at the end of her shift. However, her interest in the project has grown until she’s finally arsed herself to create a backup of the HR database on an offboard hard drive, and carry it home with her for further analysis. Of course, the original database is too big, but she’s created a random-number generator, pulled a random sampling of files (hundreds, not thousands, checking a few metrics to make sure the sampling was representative) and used them for deeper examination.

Now, she’s checking her findings against the real database again.

No one is monitoring her queries too closely because no one wants to admit to knowing what she’s up to. In fact, they trust her to be a good little Waddleduckie — though physically she’s a tall, soft, powerful person — , to provide the insights they need, and go happily back to making copies and posting notices.

It’s not a bad assumption, based on the facts at hand.

Look at her. She’s wearing a company-issued polo shirt, stained along one cuff where she’s wiped grape Kool-Ade off her lips. Her dark hair is tied back in a loose ponytail. The bangs hang past her eyebrows. Her soft oval face looks mutely — dully, one would think — at the screen. She’s wearing too-big glasses that don’t fit well. She has the barest hint of an overbite, and her front teeth overlap just a tad. She’s wearing a smear of lavender-pink lipstick, plus the Kool-Ade.

She has worked at Centaur Fulfillment for fifteen years without a problem. Advanced from picker to HR receptionist to HR assistant. She likes free donuts and meetings where they say nice things about everyone. She likes work tidily and to receive approval.

If the company had been running a slave ring out of the basement she might have thought: Well, that’s too much for me. I don’t understand it, and it’s probably wrong, but I’d better leave it alone. She would have been afraid. But this is so tiny — so mean — she just can’t stop. She is using every last scrap of unappreciated coding skills and sweet-tempered invisibility to make an algorithm for identifying potential problem pissers, which she will then use to protect those very people.

She doesn’t know how. Not yet.

Right now she is researching. She eats her way through one stale Krispy Kreme, then another. Drinks some tea, which is lukewarm and sweet with vanilla creamer. Eats a third donut. She considers creating a different, dummy algorithm for Kim, then considers against it. She does not want to cause trouble for any random subset of people, either.

She has never deliberately caused trouble in her life. But first: data.

She’s aware that data carries an undertow, that she may get lost in it. A dark austere bliss.

She'll have to get past that. Get her head above water. Maybe she’ll ask Gospel for help. Or … mentally, she thinks through her friends lists.

Her heart races. A first-in-decades panic attack.

Her palms sweat.

Hush, she tells herself. If they fire you, you can just cast a spell…

She hasn't thought like that for years.