Bibliographic record
Abstract
"The story of the individual always grips us - it is why biography remains so popular. But in Medicine and Duty we receive a double serving: the story of Medical Officer Captain Harold W. McGill coupled with the story of the many men who served in the 31st Battalion and what they together managed to achieve against such long odds." - Patrick Brennan, Centre for Military and Strategic Studies, University of Calgary Medicine and Duty is the World War I memoir of Harold McGill, a medical officer in the 31st (Alberta) Battalion, Canadian Expeditionary Force. McGill attempted to have his memoir published by Macmillan of Canada in 1935, but, unfortunately, due to financial constraints, the company was not able to complete the publication. Decades later, editor Marjorie Norris came upon a draft of the manuscript in the Glenbow Archives and took it upon herself to resurrect McGill's story. Norris's painstaking archival research and careful editing skills have brought back to light a gripping first-hand account of the 31st Battalion and, on a larger scale, of Canada's participation in World War I. A wealth of additional information, including extensive notes and excerpts from letters written "from the trenches," lends a new sense of immediacy and realism to the original memoir and provides a fascinating, harrowing glimpse into the day-to-day life of Canadian soldiers during the Great 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".