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Record W4402309225 · doi:10.3138/jh-2023-0052

“We Should Sowe and Reape in Peace”: Algonquian Foundations of Colonial Insecurity in the Correspondence of the Virginia Company

2024· article· en· W4402309225 on OpenAlexaffvenue
Joe Borsato

Bibliographic record

VenueJournal of History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsQueen's University
Fundersnot available
KeywordsColonialismContext (archaeology)ExpansionismHistoryLawAmbiguityEnglish lawCorporate governanceSociologyArchaeologyPolitical scienceLinguisticsManagement

Abstract

fetched live from OpenAlex

This article explores a case study in the history of writing about violence and destruction by examining early seventeenth century English reports concerning colonization in the Algonquian territories of the Chesapeake Bay region. An analysis of archival and printed documents from the Virginia Company of London shows that moral insecurity plagued the company members as they tried to use natural law and Roman law to justify colonization. These documents also reveal that the actions of the Powhatans and other Algonquian nations shaped this atmosphere of insecurity, doubt, and ambiguity. This article argues that from the first Anglo-Powhatan meetings in 1607 to the dissolution of the company in 1624, the company approached colonial governance and its relations with Algonquian-speaking peoples in the context of Roman law and natural law based on the principles of conquest, commerce, and occupation. These three concepts both reinforced and caused profound uncertainty for English expansionism in Algonquian territories, contributing to a tradition of scepticism toward imperial violence.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.025
Scholarly communication0.0070.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.335
Teacher spread0.281 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2024
Admission routes2
Has abstractyes

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