Bibliographic record
Abstract
Imploding the Memories Kevin Hogg (bio) 7 a.m., 3/21/04A grey cloud risesVeterans Stadium falls58 seconds end 32 yearsNow the Phanatic don’t dance here no more Home to Schmidt, Carlton, Charlie Hustleand the trophyending 97 heartbreaking yearsReduced to dust in early morningAnd the Phanatic don’t dance here no more Seats stung, turf cut and bruisedEven hot dogs disappointedBut this cookie-cutter parkwas the center of many worldsNow the Phanatic don’t dance here no more Each game someone’s pilgrimage,dream fulfilled, check on a listMy day came fall ’88Expos in, Schmidt outand a scalper unloaded skyboxFive bucks for game, food, and drinksuntil rain in the sixth [End Page 239] Redeemed by the full show the next dayTwo thrilling games, two sad memoriesfor Youppie’s out of a jobAnd the Phanatic don’t dance here no more Greater parks come, greater parks goCBP sprung up nearbyRubble cleared and pavedMarkers show the old basesbut memories lie beneathFor the Phanatic don’t dance here no more [End Page 240] Kevin Hogg kevin hogg is a teacher, writer, and Chicago Cubs fan. He holds a Master of Arts degree from Carleton University. His research interests include baseball history, and he has published sports biographies in seven encyclopedias. Copyright © 2018 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".