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Record W2559833268 · doi:10.3138/cjh.49.2.296

<i>Setting All the Captives Free: Capture, Adjustment, and Recollection in Allegheny Country</i>, by Ian K. Steele

2014· article· en· W2559833268 on OpenAlexaffvenueabout
Kathryn Magee Labelle

Bibliographic record

VenueJournal of History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsContext (archaeology)HistoryPoliticsPeacetimeQueen (butterfly)World War IILawSociologyMedia studiesPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

All the Captives Free: Capture, Adjustment, and Recollection in Allegheny Country, Ian K. Steele. McGill-Queen's Native and Northern Series. Montreal, McGill-Queen's University Press, 2013. xvi, 688 pp. $39.95 US (cloth). Ian K. Steele is not a newcomer to the history of imperial politics, military and frontiers within the Atlantic world context. Indeed, his previous works (and there are many) weave together all these topics grounded in the experiences of colonial North America. This has set the groundwork for his latest contribution. At the heart of this 688-page volume is a goal to reconstruct the world of the Allegheny country through the case studies of captives taken between the years 1745-1765 (before and during the Seven Years and what Steele calls the of 1763-1765). According to the author, by learning as much as possible about all the captives of all the contending groups, and analyzing their capture, adaptation, and fate, it is hoped that our understanding of the events, the captives and the consequences will be greatly improved (p. 3). In order to cover a wide range of topics, the book is divided into four parts. Part One: Captured in Peacetime looks at the different ways Algonquians, Iroquoians, and Europeans took and treated captives before the Seven Years War. Steele argues that the captive taking resulted in provoking the subsequent military conflicts of the eighteenth century. Part Two Captured in War looks at the captivity experience during the Seven Years and the Anglo-Indian War. He alters the perception that captives resisted and attempted to escape. Further, he contends that these wars were not total warfare, as has been described other historians. This is explained looking at raids, sieges, and battles -all of which, he argues, describe a different experience or context for captive taking. Part Three: Captivity, Conversion, and Escape looks at the physical journey to the captor's community, the process of captive selection, and specific emphasis on adoption practices among Native peoples. Equally interesting to Steele are stories of those who escaped and the initiatives to find and re-claim captives. Finally, Part Four: Setting all the Captives Free weeds out the ambiguous nature of the freed captives. It looks at ways European captives became free through gift exchange, ransom, and forcible recaptures. By looking at the objections of returning captives and their metis character, Steele questions the attractiveness of European society. Taken as a whole, Steele's latest work provides an exhaustive and comprehensive database of all those captured in Allegheny Country from 1745-1765. Through a massive survey of colonial newspapers, archival holdings, printed narratives, letters, local folklore and genealogies, Steele has managed to compile an extraordinary amount of information on approximately 6,130 people captured or killed, including: Indians, British, French, and Canadians who were traders, warriors, soldiers, farmers, wives, and children. This information is then made ready to the reader as an appendix, spanning no short of 113 pages! Each entry is organized alphabetically and includes the name (surname if provided), a physical description, and the source from which this data was collected. …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.013
Scholarly communication0.0100.009
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.009
GPT teacher head0.209
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2014
Admission routes3
Has abstractyes

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