On the Battlefront: Canadian Soldiers, an Imperial War, and America's National Pastime
Bibliographic record
Abstract
“The moment our men get out of the trenches they begin to play baseball… .” 1 1. Coningsby Dawson, Living Bayonets: A Record of the Last Push (New York: John Lane Co., 1919), 129. —Lieutenant Coningsby Dawson, an officer in the Canadian Expeditionary Force during the First World War The Great War is credited by some historians for giving direction to Canadian nationalism. Success on the battlefields provided many citizens with patriotic pride, as well as a sense of brotherhood as Canadian troops fought alongside the British in an imperial struggle. Despite an environment that favoured nationalism and imperialism, Canadian soldiers embraced America's national pastime. For many of the rank and file, baseball was an important part of their war experience. The commanding officers' support for sport, however, was essential to baseball's existence in the Canadian Expeditionary Forces. Despite the enjoyment baseball brought soldiers, a handful of officers in the military's high command were apprehensive about sport's rising status. By 1917, after years of uncertainty about how to incorporate baseball into the soldiers' training regimen, the military could no longer ignore the need and role for sport in military life. Perhaps spurred by American entry into the conflict, the CEF issued a report that officially authorized baseball and like games.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".