Bibliographic record
Abstract
No Man’s Land David Walsh (bio) [Erratum] I closed my eyes and swung, leaving success to the dumb luck of round wood meeting round ball. It traveled untouched, just the right arc over Rodney’s outstretched mitt at second base, ending in the weeds between second and right center. I was, and this was my only hit in two years of Little League, that purgatory of faded flannel handed down through the ages with names like Giants and Yankees and Braves stitched across our chests, corners of the letters picked away by years of bored fielders. Rodney still farms land across the valley, digging a living out of upstate hills. His fields are just as plentiful, the milk just as rich, as that day we faced each other across the thin grass infield, scratching out runs from walks and errors. Today, bovine milking factories cast large shadows from other valleys, and my faded line drive still falls just outside Rod’s reach, his feet inches off the dusty baseline, the victim of blind circumstance on a fenced field carved out of the valley. [End Page 101] David Walsh David Walsh has spent his career working for state and local government. He is currently serving as Chief Information Officer at the New York State Education Department. He published his first poem in 2011. Copyright © 2012 University of Nebraska 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.139 | 0.063 |
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".