“Almost Too Late”: The Merchant Navy Redress Campaign and the Struggle for Veteranhood
Bibliographic record
Abstract
This article will assess the role of Canada’s Merchant Navy during the Second World War and appraise shifting government attitudes towards the civilian mariners in veterans’ legislation in the postwar period. By examining the factors that contributed to the civilian mariners’ initial exclusion as veterans, this study sheds light on the complex process whereby the state evaluates and then reassesses what is owed to those who serve. The redress campaign to achieve veteranhood from the early 1980s provides new insight into how these veterans marshalled resources and mobilized their own personal wartime histories as part of a broader movement in that decade of re-engaging with the Second World War. As part of their efforts, the mariners first had to engage with other veterans and win them over as allies. In their struggle with the state, the mariners asserted that the risk they faced, the casualties they suffered, and the wartime government’s management of the fleet, meant that Ottawa had greater responsibilities in regard to their postwar care and that they had earned veteran status. This is the first sustained study of the redress campaign that ran for almost twenty years, from the early 1980s to 2000. It demonstrates that the concept of “veteranhood” is fluid, and that once neglected wartime narratives can be reincorporated into the nation’s military legacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".