Bibliographic record
Abstract
The Turnip Queen Ann Leamon (bio) Grandmother never prunedthe backyard forsythia,dancing wild sunbeams in April rain. Neighborscomplained when it rootedover the property line. In our front yard—turnips, jaggedgreen leaves instead of grass.Other houses on the street bloomedwith pink and blue hydrangeas,red roses, white petunias. At the prom,the popular boy gave me a bouquetof turnips, called methe turnip queen. I came home weepingin my homemade dress,the cruel turnips still clutchedin a hand that wasn't mine.Grandmother caramelized themfor a late-night snack with tea.I can still taste the sweetnessand the salt. [End Page 12] Ann Leamon ann leamon writes poetry, fiction, and textbooks about private equity. She holds degrees from University of King's College/Dalhousie, University of Montana, and Bennington Writing Seminars. Her work has appeared in the Boston Globe, Live Nudes, MicroLit Almanac, and Hole in the Head Review, among others. Copyright © 2023 University of North Dakota
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.016 |
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".