Bibliographic record
Abstract
<JATS1:p>This work chronicles the lives and accomplishments of over 200 enemies who have fought, plotted, spied on, and in some instances defeated U.S. forces over the past three centuries.</JATS1:p> <JATS1:p>Books on American military heroes abound. But this book is the first to focus on America's talentedenemies—the generals, admirals, Indian chiefs and warriors, submarine captains, fighter pilots, and spies who opposed the United States with military force or other means. Often these military leaders were among the best minds of their times.</JATS1:p> <JATS1:p>For more than two centuries, the new nation's most constant military opponents were the Native Americans, led by such capable chiefs as American Horse and Little Wolf. Under D'Iberville, Canada's French colonialists became formidable foes, but they were soon surpassed by the rigorously disciplined redcoats of Great Britain under Howe and Cornwallis. Ironically, the most effective enemies in the history of the United States were not the leaders of foreign military forces—like Mexico's Santa Anna, Japan's Yamamoto, or Vietnam's Vo Nguyen Giap. They arose from among its own citizens during the Civil War, the bloodiest conflict in American history.</JATS1:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".