Je ne me souviens pas: Pensioned Veterans from French Canada’s 22nd Battalion
Bibliographic record
Abstract
An examination of the pension files of men having served in the 22nd Battalion (canadien-français), the Canadian Corps’ only French-speaking line battalion, situates veterans into a specific ethno-linguistic and, more generally, socio-economic context. This article seeks to illuminate some of the many personal crises that could, and commonly did, afflict veterans, their families and their survivors. It demonstrates that beyond the devastation of serious physical or psychological wounding, many of Canada’s returned men, perhaps far more than we imagined, suffered persistent ill health, financial distress and family estrangement. Almost without exception, the sixty 22nd Battalion case files examined for this article revealed wounded or ill veterans’ poverty, despair, and their struggle to survive from month to month.\nThis review offers a detailed and representative cross-section of the postwar lives and pension experiences of veterans having served together and who frequently came from the same cities or regions. While no two battalions shared identical compositions and war experiences, there were broad commonalities between many of them having seen front-line service for about the same period. The findings from the 22nd Battalion veterans’ files likely would be similar to the experiences of men from many other battalions, and to those of their survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".