"Maintaining the Mobility of the Corps:" Horses, Mules, and the Canadian Army Veterinary Corps in the Great War
Bibliographic record
Abstract
Animal transportation played a crucial role in the Canadian Expeditionary Force (CEF) during the Great War. In an era of growing mechanization and technological development, horses and mules still provided the overwhelming bulk of draught power in the combat zone. They hauled artillery, supplies, and ambulances, packed ammunition, served as officers’ riding mounts, and chargers for cavalrymen. By the Armistice of 11 November 1918, the CEF alone utilized 24,134 horses and mules in France and Belgium. The task of overseeing their health and working efficiency fell to just a few officers and enlisted personnel of the Canadian Army Veterinary Corps (CAVC). Only 73 Veterinary Officers and 780 Other Ranks presided over this truly living and breathing transportation system. They treated diseases, wounds, exhaustion, malnourishment, and exposure to the elements. They saved what animals they could, and humanely destroyed those they could not. They were, in the words of Canadian Corps commander Lieutenant-General Sir Arthur Currie, essential for “maintaining the mobility of the Corps.” However, their contributions remain largely overlooked in the prevailing historiography. Neither the multifaceted literature exploring the conflict’s impact on Canadian society, nor the considerable scholarship on the CEF in France and Flanders, seriously considers the role played by Canadian horses and veterinarians in the war. A few monographs, book chapters, and journal articles acknowledge these crucial facets, though they too remain largely insular and do not connect the services of horses and veterinarians to the war’s broader chronology. This dissertation seeks to redress such omissions. It argues that Canada’s horses and mules, and the veterinary efforts to keep them healthy, exerted a clear impact on combat operations in the Great War. It explores their foundations in pre-war society Canadian to understand how both became key facets of the Dominion’s war effort, and further emphasizes the broader British Imperial context both served within overseas. Utilizing a broad array of war diaries, weekly casualty reports, government publications, internal correspondence, and contemporary periodicals from Canadian and British sources alike, this study exhibits how horses, mules, and veterinarians critically impacted Canada’s Great War experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".