Bibliographic record
Abstract
Debate still rages on about who invented baseball. But one thing is certain...it was alive and fractious in southwestern Ontario in the summer of 1949. Charlie Hodge, just finishing his last year of high school, has made the Galt Terriers’ roster and will be riding the bench with a star-studded team, many of whom had played with the major leagues. When those seasoned pros arrive in town, big things are expected, and they don’t disappoint. There is the towering home run that Goody Rosen hits into the Grand River; the frozen baseball scheme that backfires; and the busload of promotional cooking oil hijacked just before game time. It all comes down to Game 7 in the Terriers’ semi-final series with the Brantford Red Sox, when a convicted gambler, playing centre field that night, makes one of the most controversial plays ever seen at Dickson Park. Based on exhaustive research and extensive interviews, David Menary recreates that post-war season in Terrier Town through the eyes of Charlie Hodge. While Charlie is a fictional character, the other players are not. This is a story that will resonate with young and old alike, baseball fans or not. This is a team that became a vital part of the town, and the town an elemental part of the team. This is a time rapidly fading from memory — a summer of myths and legends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.666 | 0.234 |
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".