Bibliographic record
Abstract
The topics covered are wide-ranging and eclectic, and include, among others, studies of the Battle of Amiens, the Halifax explosion, Charlie Chaplin and wartime propaganda in the Canadian Expeditionary Force, Newfoundland's contribution to the war effort, the leadership capabilities of Brigadier General Griesbach, and the wartime poetry of John McRae. Contributors include Major John Armstrong (ret.), author many articles on military history and an administrative specialist in the Canadian Forces for thirty-two years, including stints as an instructor in history at the Royal Military College; Laura Brandon, curator of war art at the Canadian War Museum and co-author of Canvas of War: Painting and the Canadian Experience, 1914-1918; Patrick Brennan, associate professor of history at the University of Calgary; Tim Cook, archivist at the National Archives of Canada; Owen Cooke, independent researcher and former chief archivist at the Directorate of History, Canadian Department of National Defence; Andrew Horrall, archivist in charge of military records at the National Archives of Canada; John Hurst, retired administrator from the University of Guelph and head of the Ontario Branch of the WFA ; Jeff Keshen, associate professor of history at the University of Ottawa; David Parsons, Lt. Colonel with the Canadian Forces in Korea and chair of the Newfoundland Branch of the WFA; Roger Sarty, director of Historical Research and Exhibit Development at the Canadian War Museum; Christopher J. Terry, director, Canada Science and Technology Museums and chair of the Aviation Museum Group of the International Association of Transportation Museums; and Sidney F. Wise, professor emeritus in history and former dean of Graduate Studies and Research at Carleton University.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".