From Soldier-Poet to Veteran Memoirist: Siegfried Sassoon, The Complete Memoirs of George Sherston, and the Limits of Life-Writing in Prose
Bibliographic record
Abstract
The Complete Memoirs of George Sherston is a key text supporting Siegfried Sassoon’s reputation as Britain’s pre-eminent Great War-writer. Critics have nevertheless reached no consensus as to whether these lightly fictionalised “memoirs” represent true accounts of Sherston’s/ Sassoon’s war or fictional constructions. They have also yet to account for the differences between the Memoirs and Sassoon’s war-poetry, and between Sherston’s stated commemorative goals and his complete account. This article dissects the Memoirs’ adaptation of Sassoon’s front-line poetics of commemoration: it reads their new application of this poetics via his compositional difficulties, his dependence upon his own wartime writings, and life-writing’s uneasy relationship to truth. As I show, Sherston has more in common with his author than Sassoon intended, but differences remain; still, his memoirs have as much right to that appellation as any other text in the language.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".