Ian K. Steele. Setting All the Captives Free: Capture, Adjustment, and Recollection in Allegheny Country.
Bibliographic record
Abstract
Ian K. Steele's Setting All the Captives Free: Capture, Adjustment, and Recollection in Allegheny Country is an impressive work of research and interpretation. It makes significant contributions to studies of Native-white relations, intercultural dynamics, the Seven Years' War, Pontiac's War, and captivity narratives. For almost a century, serious study of colonists taken by Natives during the colonial era has focused on New England. Puritan captivity narratives and Emma Lewis Coleman's New England Captives Carried to Canada between 1677 and 1760 during the French and Indian Wars (1925) have provided the raw materials for generations of historians and literary scholars. With notable exceptions, these studies—until relatively recently—have had more to say about the actions and beliefs of captives than of their captors. Steele's book shifts the focus geographically to the borderlands of Pennsylvania, Ohio, and Virginia, which he calls “Allegheny country,” and gives more weight to the actions of captors. The author combed through newspapers, captivity narratives, manuscript collections, genealogies, and local histories to compile a database of 6,130 people who were killed and captured during the period from 1745 to 1765. Unlike Coleman's compilation, Steele's database of captives includes Indians, British, French, and Canadians. While he sought to record 39 variables for each individual, almost two-thirds of those killed outright or within five days of capture were not even named, and a quarter of the captives were also without names or identifying information, which introduces uncertainties into his analysis, as the author acknowledges. This database undergirds the work's approach to the subject, but the text is fashioned from documentary sources, not numbers. If anything, a few more tables would have been helpful and might have forced the author to reconcile minor discrepancies: the total number in the database is either 6,127 or 6,130 or 6,131; the total number of captives either 2,785, or 2,788 or 2,873 (which is probably a typo).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.019 |
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".