Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.000 |
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 it