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Record W4313640800 · doi:10.1017/9781108782791.003

Before the Revolution

2022· book-chapter· en· W4313640800 on OpenAlexaff
Elizabeth Elbourne

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMohawkIndigenousKinshipEmpireContext (archaeology)ColonialismAlliancePower (physics)State (computer science)HistoryBritish EmpireGenealogyEthnologyPolitical scienceSociologyLawAncient historyArchaeology

Abstract

fetched live from OpenAlex

In the context of a wider study of the evolution through time of relationships between Indigenous peoples, settlers and the British empire though the example of three family histories, this initial chapter starts in the borderlands between the lands of the Haudenosaunee and colonial New York just before the revolution. It re-reads the well-known histories of Haudenosaunee siblings Joseph and Molly Brant (Thayendenegea and Konwatsienni) and of British Superintendent of Indians William Johnson, Molly Brant’s partner, in a wider regional context. The chapter takes the Mohawk Valley as an example of a context in which the empire was compelled to accept to some extent the models of incorporation, including the creation of kinship links designed to foster mutual obligations, used by Indigenous people who were still key military allies. At the same time, William Johnson also used household power (including the ownership of enslaved people) to attempt to dominate a complex society. Before the Revolution, people in Mohawk Valley borderlands lived in a state of uneasy equilibrium, held together in part by the empire’s military need of an alliance with the Haudenosaunee, even as regional violence made relationships increasingly untenable.

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)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.024
GPT teacher head0.228
Teacher spread0.205 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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