Women’s Labour, British Naval Hospital Ships, and a System of Medical Care, 1775-1815
Bibliographic record
Abstract
This research note uses new methodologies to investigate the presence of women nurses and other labourers on British hospital ships during the Revolutionary and Napoleonic Wars. Using pay lists, musters, and log book records, it is possible to track the work of women labourers throughout the naval medical system of care. The work of women on these ships challenges our previous assumptions concerning medical care in the late eighteenth and early nineteenth centuries. Cette note de recherche fait appel à de nouvelles methodologies pour enquêter sur la présence des infirmières et des autres ouvrières sur les navires hospitaliers britanniques pendant les guerres révolutionnaires et napoléoniennes. À l’aide de listes de paye, de listes des membres d’équipage et de registres des journaux de bord, il est possible de suivre le travail des femmes dans tout le système de soins médicaux de la marine. Le travail des femmes sur ces navires nous oblige à revoir nos suppositions antérieures concernant les soins médicaux à la fin du 18e et au début du 19e siècle.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".