Ken Miller. Dangerous Guests: Enemy Captives and Revolutionary Communities during the War for Independence.
Bibliographic record
Abstract
There have been many book-length studies on colonies and large cities during the American Revolution but few about the smaller burgs that witnessed the great upheaval of our nation's founding. On the face of it, Ken Miller's Lancaster, Pennsylvania, is a bustling rural town of 3,000 people. It never incited parliamentary action, never boasted a major battle, and never produced a founding document. Yet this community, just 60 miles west of Philadelphia, had more contact with the enemy than practically any other Whig-controlled place in the 13 fledgling states. The farmers and artisans of Lancaster shouldered the enormous responsibility of housing prisoners of war from the 1775 Canadian campaign to Yorktown. These “dangerous guests” at times numbered one-third of the town's population, keeping Lancaster on alert until the end of 1782. The book starts with pre-revolutionary times when German speakers, numbering 70 percent of Lancaster's families, clung to their homeland culture much to the dismay of the English speakers in town. Confronted with the French and Indian War, however, the Lancastrians came together to deal with the crisis. Still, claims Miller, most settlers “remained mentally rooted to their locale, their concerns rarely straying beyond county or province” (p. 39).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".