Bibliographic record
Abstract
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. 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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".