Bibliographic record
Abstract
Why do soldiers fight? What keeps them going? What compels them to face death when their long-time comrades have fallen around them? Strangers in Arms addresses these questions in a groundbreaking study of the behaviour, morale, and motivations of Canadian infantrymen on the front lines of the Second World War. Canada’s army has long faced intense criticism for its combat performance during the war, and Canada’s official history has presented Canadian soldiers as deficient, inexperienced, and unprepared in comparison with their enemies. Questioning entrenched views, Robert Engen explores a trove of contemporaneous documents to create a remarkable new portrait of Canadians at war. Rather than the popular "band of brothers" image of soldier cohesion in battle, he finds staggering casualty rates and personnel turmoil that left Canadian infantrymen often working with and fighting beside men they hardly knew. Yet these strangers in arms continued to fight - effectively and in good spirits - against a tenacious and deadly enemy, triumphing in the face of heartrending loss and sacrifice. Challenging old narratives about the Canadian soldier and supported by cutting-edge empirical and qualitative research, Strangers in Arms crafts a new understanding of what happens at the sharp end of battle.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".