Bibliographic record
Abstract
The Echoes David Eldridge (bio) “Get your program!” The bustling of fans on the concourse rushing to get to their seats while they chat about yesterday’s game and today’s starting pitchers. The smack of the ball hitting the catcher’s glove in the bullpen as the pitcher warms up. The cheers of the fans for the ceremonial first pitch. The antics of the team’s mascot. The crowd singing the National Anthem. “Play ball!” The crack of the ball hitting the bat for a home run. The cheers or boos of the hometown fans when the ball clears the fence. “You’re out!” “Safe!” The melodies from the stadium’s organ. The vendors yelling “Beer here!” or “Popcorn, get your popcorn!” The slinging of a peanut bag to a fan. “Take me out to the ball game.” [End Page 1] The final out. The cheers of the fans of the winning team. The bustling of fans on the concourse rushing to leave while they chat about today’s game and tomorrow’s starting pitchers. “See ya tomorrow.” [End Page 2] David Eldridge david eldridge is a baseball fan and a poet. While staying-at-home due to COVID-19, he was thinking about the images, sounds, and experiences he missed from the ballpark and decided to give them life in a poem. Copyright © 2021 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.237 | 0.121 |
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".