Bibliographic record
Abstract
Washed Clean Ann Leamon (bio) Springtime on the Maine farm where I grew up meant melting. The smell of manure and mud mixed with the tang of smoke as maple sap boiled off to syrup. It meant lambing, too; my mother waking at two in the morning to check on the ewes. Most of them, dirty white skeins of unspun wool on skinny black legs, birthed their twins without trouble, even in the dirt-floored lean-to, but Granny, the matriarch—a scruffy, stubborn blend of unknown breeds—had triplets year after year. Sometimes the three could manage; alternatively, we bottle-fed the smallest, keeping it in a box behind the kitchen woodstove. In the dark March nights, often with snow or freezing rain, my mother pulled on her boots and jacket and checked on the sheep. Granny always lambed between the checks. One night during a fierce snowstorm, Granny lambed. In the harsh light of the two a.m. flashlight, Mom found two tiny figures, snuggled against the old ewe just as they should be. Maybe, this time, she’d only had the two. But at the morning feeding, under a drift of snow, we found the third, a cold scrap of pure white fluff. Oh, lamb of God. All creatures great and small. Remembering James Herriot, Mom put the small body in a bucket of warm water. It floated like a bar of Ivory soap. After five minutes, the lamb opened his eyes. [End Page 25] Ann Leamon Ann Leamon’s world includes venture capital research and creative writing. The sheep are gone, although she loves to watch them in the spring. In addition to writing a textbook and 150 cases for Harvard Business School, she has published in The Lyric, Hole in the Head Review, Live Nudes, Microlit Almanac, They Call Us…, and The Boston Globe. She holds degrees in German, Economics, and Poetry from Dalhousie, the University of Montana, and the Bennington Writing Seminars, respectively. She lives on the coast of Maine with her husband and a Corgi-Lab mix. Copyright © 2023 River Teeth
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.701 | 0.573 |
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".