Bibliographic record
Abstract
Change of Equation Sydney Lea (bio) There's a list of everyday chores taped to the dash of my trailworn Subaru, which our kids call Old Stinky for the reek of pipesmoke and mud and fish—and four gundogs. So what else would top the list but dog food? The beasts eat us almost poor, but because they are fine hunters I buy their provisions from the vet's clinic, rather than giving them the cheap crap you can pick up even at wretched Wal-Mart, which isn't here yet, nor please God will be in my lifetime. The bag feels heavy, and my shoulders weak, sore. I'm getting old. Pinch me, I am old. Then, Bank. As I pull into the lot, I think once more, "Where does the cash go?" Today it goes on a trip with our youngest daughter who's off south with school friends to some amusement park that years ago destroyed its neighborhood's deep woods and waters. Our taxes are high and rising. And wouldn't you know it? At the teller's window I don't get our friend Paula but some stranger who appears to think me a bandit. Choiceless, I surrender photo identification, even though I was living and banking here before this woman drew breath or plucked an eyebrow. (Her makeup's louder than the log truck idling outside.) By now you'd think I was past suspicion. [End Page 1] With my wallet full, I get back in Old Stinky and read Wallace. He's a stroke-blighted man in the County Home, who, our preacher thought, might enjoy my company, most of his fellow residents being women. Wallace has no living kin. I remind myself once again that when he wants to get up from his chair, I must not help him I must not help him I must not help him. There's such a thing as dignity. I hope so anyhow. I pass the other stinking old rooms till I get to his. He shakes my hand with a smile that will light the dismal hallways, struggles up, alone, and we take our little tour of those halls till he tires. Wallace rests and recalls, as he has again and again on my visits, how he was strolling Route 5 one day as a younger man and saw the biggest bird in the world. He tells me how it stooped to the tilth, grabbed something, then climbed back across Black Mountain on the New Hampshire side of the river. End of story, it seems. What does he make of the memory? Nothing—or else he can't or won't say. But his smile has gone even brighter for the telling. I mean Wallace well, of course, but I also need to get home. My wife and I have things to do—perhaps with the children. But I'll see to the haircut, last on my list. Not much to cut anymore, but that's the least of my worries, and after all I like to visit Paul the barber. How would I have remembered that today is the anniversary of his two grandsons' drownings? But that's what it is. Paul's the one with the worries, above all for his heartbroken wife, who, he says, is almost over the edge. [End Page 2] So we change the subject. Paul speaks of his trip to Derby, right tight to the Canada border above us, where he'll soon be throwing in a horseshoe tournament with some other old boys. "I'm good, if I say so," he boasts. At that, the farmer waiting his turn asks him, "Paul, anyone ever accused you of usin' them stee-roids?" I'm still chuckling as I drive homeward, though I can feel a kind of bleakness under my own laughter. Sometimes it seems that life is just one foot ahead of the other. At least my list is now only a set of crossed out lines. I ought to be thinking, say, that one foot ahead of another is just fine by Wallace; but I'm not thinking that. I park at home, lace on...
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.215 | 0.041 |
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".