Lexicon

keypad-protected doors between her and anyone more senior than a receptionist. They interviewed people down here, she knew: attached them to probes and ran them through fMRIs to record what happened when they heard words. Then they sent the data upstairs to NL for analysis. Where these test subjects came from, she didn’t know. Although once while looking for a pay phone near George Washington University, she had seen a paper stapled to a light pole offering fifty dollars for volunteers for a psychology experiment, so maybe that. When the data came through the ticketing system, sometimes under OBERVABLE EFFECTS it said
psychotic break
, or
loss of function
, or
coma
. She tried not to think about this too much. But it was obvious that people got hurt down there.
    • • •
    Sashona—
Smith
, as Emily would never feel comfortable calling her—had changed a lot. She laughed, which she had never done at school, and found everything
amazing
. This struck Emily as unlikely behavior, since Sashona should have been guarding her personality to prevent segmentation. She decided it was feigned: a behavioral smoke screen. The higher levels didn’t do this; Emily had talked with Eliot plenty and had no idea of his segment simply because he gave nothing away. But it made sense for a newer poet. It made her wonder if she should be doing the same thing, and if Sashona thought Emily was trying to figure out her segment, and if Sashona was trying to figure out hers.
    One day, as a tall, handsome barista delivered coffees to their café table, Sashona opened her mouth and a snarl of unintelligible words tripped out. “Love me,” Sashona said, and the barista spilled the coffee and went away and came back to ask for Sashona’s phone number. This was how Emily discovered that in the four years she had been selling blouses in the desert, Sashona had been learning words. Emily murmured her appreciation, but the truth was she was shocked. She hadn’t realized how far behind she was. How was she supposed to catch up? She had no one to ask but Sashona, and although they were friendly, she was afraid to expose her ignorance.
    She decided to hope that one day somebody would appear to educate her. In the meantime, she read data and tried to pound it into thoughtful conclusions. The organization was interested in refining its psychographic model, in finding ever-better ways to classify people more accurately into fewer segments. She looked for responses in graphs that shouldn’t be there, tiny bumps in blue lines, and wrote reports on possible psychographic overlaps, and segment boundary blurring, and possible new avenues for segmentation. She had access to a vast database of shopping habits, Internet usage patterns, traffic flows, and more; if she wanted, she could drill right down to an individual and look up where they went last Tuesday and what they bought and did. But that was not very useful. No one was interested in individuals. She was supposed to look for connections between them: neurological commonalities that allowed them to be grouped together and targeted by a common word. Whether anybody acted on her work, or even read it, she had no idea.
    It became harder to find a pay phone she hadn’t used to call Harry before. Every night, as she walked the streets, she half-expected Eliot or Yeats or maybe that kid in the airy suit to step out of the darkness. And then everything would be over. But that never happened, so she kept doing it.
    • • •
    One day she got a corrupted data set from a ticket, so she picked up the phone and dialed Labs. She was not supposed to do this. At least, she was supposed to do it as little as possible. Techs were isolated from analysts for security reasons, since techs were not poets and were therefore vulnerable to compromise. Why an analyst might want to compromise a tech, she had no idea. It seemed pretty pointless. But that was the rule. It didn’t seem very effective, either, since although the techs were supposed to be anonymous, they gave themselves away in their writing styles: one overused
evidently
, one had never heard of apostrophes, that kind of thing. So she did not have a great deal of respect for the rule.
    “Hello,” she said when Labs picked up. “This is Analyst three-one-nine. I need a validation check on a data set, please.”
    “Open a ticket,” said a male voice. She had seen no evidence of women in Labs.
    “I did open a ticket, and it came back the same. I want