Bibliographic record
Abstract
It was the “Good War.” Its cause was just; it ended the depression; and Canada’s contribution was nothing less than stellar. Canadians had every reason to applaud themselves, and the heroes that made the nation proud. But the dark truth was that not all Canadians were saints or soldiers. Indeed, many were sinners. In this eye-opening and captivating reassessment of Canadian commitment to the cause, some disturbing questions come to light. Were citizens working as hard as possible to back the war effort? Was there illegal profiting from the conflict? Did Canadian society suffer from a general decline of “morality” during the war? Would women truly “back the attack” in new factory jobs and the military, and then quietly return home? Would unattended youth produce a crisis with juvenile delinquency? How would Canada reintegrate a million veterans who, policy-makers feared, would create a social crisis if treated like their Great War counterparts? The first-ever synthesis of both the patriotic and the problematic in wartime Canada, Saints, Sinners, and Soldiers shows how moral and social changes, and the fears they generated, precipitated numerous, and often contradictory, legacies in law and society. From labour conflicts, to the black market, to prostitution, and beyond, Keshen acknowledges the underbelly of Canada’s Second World War, and demonstrates that the “Good War” was a complex tapestry of social forces – not all of which were above reproach.
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 | 0.044 | 0.024 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".