Transnational Volunteers: A Research Note on Border-crossing to Enlist in the American Civil War
Bibliographic record
Abstract
Historians of transnational and global relations have increasingly reminded us that fighting in “someone else’s war” is not a new phenomenon. Despite the past two centuries being ones of state building and bordering, transnational volunteers have participated in many conflicts. These include soldiers who left British North America (bna) to enlist in the American Civil War. This research note describes a method for distinguishing cross-border enlistments from the many bna-born immigrants already living in the United States. By cross-referencing online data archives, it was possible to create an original database of bna-born individuals who lived in the Eastern Townships of Canada East in 1861 and crossed the border to enlist in Vermont. Next, a comparison of patterns of enlistment behaviour shows the transnational volunteers’ actions closely tracking those of native-born and naturalized Vermonters. Both groups followed the same trajectory, with high rates of volunteering in early months, a decline, and then a spike in late 1863, when federal authorities deployed more generous bounty payments as a policy instrument to alter the incentive structure for enlistment. Nonetheless, seventy percent of these bna residents had already enlisted before September 1863. This note suggests that transnational volunteering from bna to the Civil War merits further attention from both comparative and transnational historians.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".