Bibliographic record
Abstract
The public tends to think of war memorials as fixed monuments, but I argue that the American and Canadian Legions served as living memorials that acknowledged veterans’ war-time service by providing service to veterans and to the public. This dissertation focuses on how Legionnaires interacted with one another and with their local communities during the interwar years to construct memories of the First World War. By analyzing local chapter records from Michigan, New York, and Ontario, Canada, this case study highlights the contrast between the organizations’ national and local activities. The local posts’ and branches’ wide range of activities complicated the national organizations’ collective memories of the First World War. A new way to construct a holistic depiction of veterans’ organizations is to study them as living memorials. From this perspective, all of their day-to-day activities fulfill the larger purpose of preserving and perpetuating the memory of their war experiences. At the national level, the American and Canadian Legions advocated for legislation to benefit veterans, but it was primarily at the local level where rank-and-file members shaped the Legions’ collective memories of the war. This study explores elements of those memories, including sacrifice, service, and camaraderie, through the tensions that sometimes arose between the national leadership and the local chapters and compares the American and Canadian Legionnaires’ experiences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".