Writing a Canadian High School History of the Great War: Victoria High School: Challenges, Pitfalls, and Sources
Bibliographic record
Abstract
Contained in this article are suggestions on how to write a history of a Canadian high school and the First World War. Included in the discussion is the approach and methodology of the historian, the materials available for use, and knowledge of the background of Canada, the British Empire and the war “for King and Country.” It appeals for an understanding of war and of patriotism Canadian-style as of 1914-1918, as a war they fought and not the one we now think they fought or should have fought. It is an appeal for “sharp end” history. Attention is given to monuments of valour – rolls of honour, plaques, banners, stained glass, gravestones and markers, memorial trees and, above all, school records. The history should be a tribute to a youth now no longer with us. The history, when written, becomes its own memorial to their passing and sacrifice and may serve as an example for other such histories to be crafted. Lastly, it is a legacy to a grey generation of mothers, sisters and sweethearts who far from the searing battle line were also victims in this catastrophic eruption that forever changed Canada and, through what I call the Vimy Alchemy, made a nation in an age of dissolving empires. Above all, keep school records related to this war and to others. Years hence other historians will be grateful.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".