Bibliographic record
Abstract
For decades, Canadian and Newfoundland Voluntary Aid Detachment (VAD) nurses have remained in the shadows of First World War histories.Although no Canadian or Newfoundland VAD published her own account of the war, some did leave traces of their experiences either tucked into archival collections or stored in the family attic.With the anniversary of the war, it is time to bring the history of these VADs into the light.This book began at the University of Ottawa in the 1990s, when historian Ruby Heap discovered a photograph of a woman in a VAD uniform.The image led me to the headquarters of the St. John Ambulance Association in Ottawa, which generously opened its archive collection and library to initiate my research.What appeared to be a small organization of fewer than a hundred women was gradually revealed to include well over a thousand individuals whose uncharted history ran parallel to that of Canadian military nurses.It has been my privilege to uncover the unique experience of the Canadian and Newfoundland VADs and to restore their place in the history of Canada's war.There are many people and organizations to acknowledge for their generous support and interest in this project.First, of course, I must thank Ruby Heap, a friend and colleague, for her invaluable mentoring.My sincere appreciation also goes to the St.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.182 |
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".