Overcoming the Odds: A Comparison of the Ninth and Tenth Military Districts During the Final Campaigns of the War of 1812
Bibliographic record
Abstract
During the first year and a half of the War of 1812 the United States Army fought with little success against a professional British Army and Canadian Militia who lacked troops and supplies due to the ongoing Napoleonic Wars. In October 1813 Great Britain's allies had defeated Napoleon at the Battle of Leipzig. With victory in Europe behind them, the British began diverting battle-proven troops and supplies to North America. The perception of this policy changed the complexion of the war to heavily favor the British in numbers of experienced and battle-hardened troops. By comparing the Ninth and Tenth Military Districts the question this study will investigate is "How did the United States Army prepare to face the Napoleonic War veteran British Army during the last year (1814) of the American War of 1812?" The two factors that were most imposing on them during this preparatory phase, besides the enemy, were support and political-military relationships. Critical to this study is the political-military relationship between the Secretary of War and his military district commanders. Additionally, the War of 1812 will be used as an example to help the United States understand and gain insights from history about how to initiate Homeland Defense today.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".