Medical response to the declaration of the First World War: The case of Edwin Seaborn
Bibliographic record
Abstract
At the turn of the 20th century, Dr Edwin Seaborn was starting his surgical and academic career at Western University in Ontario. When war was declared in 1914, Seaborn prevailed upon the university's president to offer the Canadian government a fully staffed hospital for deployment overseas. Initially declined by the War Office in Ottawa, the university's offer was later accepted after mounting casualties stretched the capacity of the Canadian Army Medical Corps, and Seaborn was granted command of the new No. 10 Canadian Stationary Hospital. From 1916 to 1919, Seaborn's medical, surgical, and administrative practices transformed the humble No. 10 Stationary Hospital into a General Hospital that was indispensable to the war effort and raised the standard for military medical practice. Upon the unit's return to London, Ontario, Seaborn's dedication was transferred to his extensive work as an author, historian, academic, and beloved physician. During the centennial of the First World War, this paper explores the impact of an academic medical unit by looking at the career of its Commanding Officer: a man who made an invaluable contribution to the Canadian war effort and set a precedent for exceptional medical care at home and at war.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.019 |
| 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".