Uniforms and Commercial Culture: Constructing a Vision of Warfare in Pre-Great War Britain
Bibliographic record
Abstract
Uniforms carry cultural meaning shaped by their interaction with military realities. They can communicate tradition but also anticipate change. Prior to the Great War, British Army uniforms had developed from the familiar red tunic to khaki, but the manner of their representation in the mass culture confirmed a continuity and correctness of the British way of war that ran against the emerging industrialization of warfare. Wearing familiar uniforms linked to the past and concurrently fighting what seemed like anachronistic ‘small wars’ in empire as reported in the press, what awaited the volunteers of 1914–15 could not have been anticipated by those consumers of the commercial culture. This article uses a variety of sources, from the illustrated adult and juvenile press, paintings, and toys, to reveal the link between uniforms and the representation of warfare in the fifty years prior to the Great War. In that representation we see not just the glorification of war that cultural historians attach to gendered, imperialist, or nationalist meanings. This article argues that the role of uniforms in the representation of warfare was a means by which to make it knowable and worthwhile for the consumer public. But by representing past and contemporary uniforms quite accurately, the writers and artists imposed a sense of military continuity at a time when war was changing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".