White Man's War, Coloured Man's Labour. Working for the British Army on the Western Front
Bibliographic record
Abstract
The Great War was indeed a world war. Imperial powers like Great Britain drew on their far-flung empires not only for resources but also for manpower. This essay examines one important (though still inadequately studied) aspect of British wartime exigency, the voluntary and coerced participation of the British Empire's coloured subjects and allies in military operations on the Western Front. With the exception of the Indian Army in the first year of the war, that participation did not include combat. Instead coloured troops, later joined by contract labourers, played major roles behind the lines. From 1916 onwards, well over a quarter million Chinese, Egyptians, Indians, South Africans, West Indians, New Zealand Maoris, Black Canadians, and Pacific Islanders worked the docks, built roads and railways, maintained equipment, produced munitions, dug trenches, and even buried the dead. Only in recent years has the magnitude of their contribution to Allied victory begun to be more fully acknowledged. Yet the greatest impact of British labour policies in France might lie elsewhere entirely. Chinese workers seem likely to have carried the virus that caused the Great Flu pandemic of 1918-19, which may have killed more people around the world than the war itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".