The Poetry of the Great War and Propaganda
Bibliographic record
Abstract
The main objective of this thesis is to analyze some examples of First World War poetry regarding, on one hand, its function as propaganda and pro-war material, and on the other hand, its function as anti-war material. It demonstrates how the Great War influenced and challenged the poetic conventions of the period, but also how poetry influenced the war. The poets examined include Rudyard Kipling, Rupert Brooke, Jessie Pope, Robert Graves, Siegfried Sassoon, and Wilfred Owen. The first three of the six authors are the examples of pro-war poetry that illustrate some similar aspects of the pre-war and war poetry that is patriotic and that romanticizes the idea of war. Consequently, it is important to explore the concept of propaganda, and specifically, the occurrence of propaganda during the First World War through Peter Buitenhuis’s The Great War of Words: British, American, and Canadian Propaganda and Fiction, 1914-1933. Furthermore, various articles by authors such as Jo Fox, Alice Goldfarb Marquis and Anurag Jain will serve as a link between propaganda and literature. Meanwhile, the latter three authors are some of the more prominent authors of the Trench poetry, particularly, they are the representatives of the anti-war poetry after experiencing the horrors of the trenches. Moreover, Susanne Christine Puissant’s Irony and the Poetry of the First World War, Jon Silkin’s Out of Battle: The Poetry of the Great War, and David Punter’s Literature of Pity provide the theoretical framework for the analysis of the poems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".