Bibliographic record
Abstract
As patron of the Hill 70 Memorial Project, I am pleased to be associated with the publication of a book devoted to revealing, at long last, the history of the Battle for Hill 70, which took place in August 1917.The nine contributors to this important volume have fashioned a story that highlights the achievements of Canada's famed Canadian Corps at an important moment during the First World War.With the Battle of Passchendaele raging farther north in Flanders, the Canadians' task was to pin down enemy forces in and around the city of Lens in northern France.They would accomplish this by seizing Hill 70, the key high ground that protected the city.There were approximately nine thousand Canadian casualties between August 15 and 25, 1917.Six Canadians earned Victoria Crosses.Yet, despite the losses, Canadians succeeded in their mission, due to meticulous preparations and to the leadership of their commander, Lieutenant-General Sir Arthur Currie, who, at Hill 70, fought his first battle as commander of the corps.He would go on to orchestrate many other Canadian victories before the war was over.Canadian successes in the war, Hill 70 among them, helped forge a growing sense of national identity, which culminated in Canada shedding its colonial status and joining the family of nations in its own right.I thank the Hill 70 Memorial Project for its splendid work in bringing the battle for Hill 70 to the public's attention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.567 | 0.534 |
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".