Bibliographic record
Abstract
“I want you to promise me something,” she says. She takes her hand back. She moves my arm away from her shoulder. “I want you to promise me you'll pull the plug on me, if and when it's ever necessary. If it ever comes to that, I mean. Do you hear what I'm saying? I'm serious about this, Jack. I want you to pull the plug on me if you ever have to. Will you promise?” I don't say anything right away. What am I supposed to say? They haven't written the book on this one yet. I need a minute to think. I know it won't cost me anything to tell her I'll do whatever she wants. It's just words, right? Words are easy. But there's more to it than this; she wants an honest response from me. And I don't know what I feel about it yet. I shouldn't be hasty. I can't say something without thinking about what I'm saying, about consequences, about what she's going to feel when I say it—whatever it is I say. I'm still thinking about it when she says, “What about you?” “What about me what?” “Do you want to be unplugged if it comes to that? God forbid it ever does, of course,” she says. She hasn't moved. She's still waiting for her answer. And I can see we're not going anywhere this morning until she has an answer. I think about it some more, and then I say what I mean. “No. Don't unplug me. I don't want to be unplugged. Leave me hooked up just as long as possible. Who's going to object? Are you going to object? Will I be offending anybody? As long as people can stand the sight of me, just so long as they don't start howling, don't unplug anything. Let me keep going, O.K.? Right to the bitter end. Invite my friends in to say goodbye. Don't do anything rash.” “Be serious,” she says. “This is a very serious matter we're discussing.” “I am serious. Don't unplug me. It's as simple as that.” She nods. “O.K., then. I promise you I won't.” She hugs me. She holds me tight for a minute. Then she lets me go. She looks at the clock radio and says, “Jesus, we better get moving.” The phone rings. We let go of each other, and I reach to answer it. “Hello,” I say. “Hello, there,” the woman says back. It's the same woman who called this morning, but she isn't drunk now. At least, I don't think she is; she doesn't sound drunk. She is speaking quietly, reasonably, and she is asking me if I can put her in touch with Bud Roberts. She apologizes. She hates to trouble me, she says, but this is an urgent matter. She's sorry for any trouble she might be giving. While she talks, I fumble with my cigarettes. I put one in my mouth and use the lighter. Then it's my turn to talk. This is what I say to her: “Bud Roberts doesn't live here. He is not at this number, and I don't expect he ever will be. I will never, never lay eyes on this man you're talking about. Please don't ever call here again. Just don't, O.K.? Do you hear me? If you're not careful, I'll wring your neck for you.” “The gall of that woman,” Iris says. My hands are shaking. I think my voice is doing things. But while I'm trying to tell all this to the woman, while I'm trying to make myself understood, my wife moves quickly and bends over, and that's it. The line goes dead, and I can't hear anything.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.456 | 0.162 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".