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Record W2337484610 · doi:10.1093/ahr/120.3.1013

Ken Miller. Dangerous Guests: Enemy Captives and Revolutionary Communities during the War for Independence.

2015· article· en· W2337484610 on OpenAlexaboutno aff
Judith Van Buskirk

Bibliographic record

VenueThe American Historical Review · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMillerBattleHomelandAdversaryIndependence (probability theory)Spanish Civil WarLawPopulationEconomic historyHistoryPolitical scienceSociologyAncient historyPoliticsDemography

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.193
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.259
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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