Bibliographic record
Abstract
The Canadian War Museum has always been the Cinderella of Canada's national museums.Although it has existed in various forms for more than a century, although its collections of art, military vehicles, weapons, primary documents, photographs, military clothing, and medals are truly extraordinary, the Museum suffered from substandard accommodation in Ottawa and a long tradition of weakly researched exhibits.For too long, the War Museum's exhibits were a heap of artifacts rather than a researched, coherent, and historically sound approach to Canada's long military history.The government's decision in 2000 to build a new museum at last will bring the War Museum to the forefront of Canada's national museums.One area, however, was not neglected in the past: scholarship.In the dark days, the Museum staff created a Historical Publications series that helped greatly to encourage scholarly publication in Canadian military history.But this series, which was issued by many publishers, eventually succumbed under the weight of budget cuts.When I went to the CWM as Director and CEO on 1 July 1998, one of my primary goals was to establish the Museum's place as a research institution and to restore its role as an encourager of scholarly research and publication in military history.This volume, the first in what I trust will be a long association with the University of British Columbia Press, genuinely marks a new beginning.Full scholarly standards, including peer review, have been scrupulously followed in
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.611 | 0.555 |
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".