Portrayals of Suffering: Perceptions of Trauma in the Writings of First World War Nurses and Volunteers
Bibliographic record
Abstract
The trauma-writings of World War I nurses have been identified as an important and influential corpus of early 20th-century works. Not only did the rediscovery of these writings in the later 20th century serve to recognize the importance of women's writings as part of the historical record, and identify certain female writers as some of the most important thinkers of the modernist movement; they also demonstrated the importance of the nursing perspective as one element of wartime experience. This paper considers a number of influential works written by both nurses and members of the Voluntary Aid Detachments (VAD) who assisted with nursing work during the war. The paper identifies how nurses and VADs presented their experiences of war trauma. It also considers how some writers strove to attach meaning to (or in some, cases expressed their sense of the meaninglessness of) the suffering caused by the war. The paper considers further how some nurses themselves experienced trauma as a result of their exposure to wartime work, and how some writers developed what are referred to as "philosophies of suffering," in which they struggled to understand suffering as an element of human experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".