Bibliographic record
Abstract
"Why does Vimy matter? Tim Cook, Canada's foremost military historian and a Charles Taylor Prize winner, examines the battle of Vimy Ridge in April 1917 and the way the memory of it has evolved over 100 years. Vimy is unlike any other battle in Canadian history: it has been described as the "birth of the nation." But the meaning of that phrase has never been explored, nor has any writer explained why the battle continues to resonate with Canadians. The Vimy battle that began April 9, 1917, was the first time the four divisions of the Canadian Expeditionary Force fought together. 10,600 men were killed or injured over four days--twice the casualty rate of the Dieppe Raid in August 1942. Cook has uncovered new material and photographs from official archives and private collections across Canada and from around the world. Many of these resources have never been used before by other historians, writers, or film-makers. On the 100th anniversary of Vimy, and as Canada celebrates 150 years as a country, this new book is about more than a defining battle: it is a story of Canadian identity and memory, by a writer who brings history alive."--
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".