How to Tell the War? Trench Warfare and the Realist Paradigm in First World War Narratives
Bibliographic record
Abstract
This paper will analyze how memoirs and novels of the First World War reflect the challenges which modern warfare poses to realist narrative. Mechanized warfare resists the narrative encoding of experience. In particular, the nature of warfare on the Western Front 1914–1918, characterized by the fragmentation of vision in the trenches and the exposure of soldiers to a continuous sequence of acoustic shocks, had a disruptive effect on perceptions of time and space, and consequently on the rendering of the chronotope in narrative accounts of the fighting. Under the conditions of the Western Front, the order-creating and meaning-creating function of narrative seemed to have become suspended. As I want to show, these challenges account for a fundamental ambivalence in memoirs and novels which have largely been regarded as paradigmatically ‘realistic’ and ‘authentic’ anti-war narratives. Their documentary impetus, i.e. the claim to tell the ‘truth’ about the war, is often countered by textual fragmentation and a “cinematic telescoping of time” (Williams 29), i.e. by a structure which implies that such a ‘truth’ could not really be articulated. In consequence, these texts also explore the relationship between fact and fiction in the attempt at rendering an authentic account of the modern war experience. My examples are Edmund Blunden’s Undertones of War (1928), Robert Graves’s Goodbye to All That (1929) and the novel Generals Die in Bed (1930) by the Canadian Charles Yale Harrison, as well as German examples like Ernst Jünger’s In Stahlgewittern (1920; The Storm of Steel, 1929), Ludwig Renn’s Krieg (1928; War, 1929) and Edlef Köppen’s Heeresbericht (1930; Higher Command, 1931).
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".