“The Entire Army Says Hello”: Common Soldiers’ Experiences, Localism, and Army Reform in Britain and Prussia, 1739-1789
Bibliographic record
Abstract
This dissertation fundamentally questions the state of the field regarding militaries, state building, and narratives of modernity in the Kingdoms of Britain and Prussia. An examination of military stereotyping, common soldiers’ correspondence, religion, localism, and army reform all suggests that the British and Prussian militaries were mutually-intelligible and similar, not radically different. This similarity has broad implications for the modern history of these two European states. Britain was not on a straight road to whiggish parliamentary progress, and Prussia was not on a straight road to militarism and authoritarian rule. Rather, in second half of the eighteenth century, both of these military-fiscal states utilized their religious rural subjects, drawn from their village communities, as the basis of their military strength. Forming part of a growing scholarly revolution regarding eighteenth-century common soldiers, this dissertation relies on soldiers’ letters drawn from archives across the United Kingdom, Germany, and the Atlantic World. “The Entire Army Says Hello” demonstrates that soldiers in Britain and Prussia experienced broad similarities in their military service, and those similarities offer a new framework for the national history of these two states.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".