A Poet Listening to a Baseball Game Late at Night, and: Losing Streak
Bibliographic record
Abstract
A Poet Listening to a Baseball Game Late at Night Go Mets. They're leading, 6-0, not a bad way to end the day. Piazza has recovered nicely from his latest injury. Leiter has an easy job of it. They're playing the Diamondbacks under a western sky sparkling with those same stars cowboys see, like a scorecard in May when the season is young and the team is full of energy, promising another summer of pleasures on my little radio, a Sony I bought for only ten bucks, my silvery link to rows of those stars so far away and near. This piece of paper, that whack of a bat, a rustle, a synergy, far from my desk and yet close to my heart. Nothing really important. Hey, they're hitting homers up to the Milky Way. This game is pure poetry. [End Page 120] Losing Streak First hurricane of the season. Water and wind slap windows of the diner. Here I sit in the diner toying with dinner. Our howling storm focuses my mind on how an extra "n" makes the difference between bacchanalia and banality. Extra innings. If only, if only my team had stayed the course. Of course, they lost the pennant. No deluge of champagne. Waves of rain cascade down dirty glass. The outfield grass is beaten down. Think of analogies. Watery gravy escapes. This mushy grave cannot contain it. Mashed potatoes are a dubious mitt against the torrent. Soon, the eye of the storm. Puddling. Hunger pokes at the catcher. Rivulets of moisture slide down a tumbler. Yet another supper. Super storm, eh? What do you recommend for dessert? What for the pain? Slide, slide, beat a wild throw home.
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.342 | 0.193 |
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".