Bibliographic record
Abstract
Deflection Mechanism #1 Austin Tucker (bio) Yes, come in. I've made risotto.Scallops too, massive onesjust for this occasion. Convention says the secret is fresh mozzarella,spices picked straight from the garden,but I prefer that feeling you get remembering a time too far away for the details to be correct:a view from the porch where the light undoesthe alders like shoelaces, the scrolling end-credits of a movie you'll forget you've seen,names bright like out-of-focus stars,the cedar smell of an antique shop settling in the dust of its old cameras.Really, though, the secret is to notget distracted. You can really burn a risotto when you're distracted.Distractions are, somehow, unforgettable,and they can last for years, much longer than cooking a risotto. So enoughof that. I've pulled out the Princess Houseglasses, the clearest crystal you've ever seen. A toast–and yet we go on. Please,as my mother would say:I've made so much food it's a kind of grammar. [End Page 49] I cut the ham straight from the bone,and I promise the soufflé is like going on holiday.Once I told someone I loved I lived in Arizona, just two streets down from Miami.I haven't thought of her in years and, by the way,the fish is lovely, cooked as if it felt nothing at all. [End Page 50] Austin Tucker Austin Tucker received his MFA in poetry from Rutgers-Camden. His poetry has appeared in The Orange Coast Review, Four Chambers, and Frontier, and was a semifinalist for the 2018 Halifax Ranch Prize and long listed for the 2019 Disquiet International Literary Contest. He lives in Philadelphia. Copyright © 2022-2023 Pleiades and Pleiades Press
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.664 | 0.402 |
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".