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Record W4313640763 · doi:10.1017/9781108782791.001

Introduction

2022· book-chapter· en· W4313640763 on OpenAlexaff
Elizabeth Elbourne

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousKinshipColonialismScholarshipPolitical scienceConsolidation (business)GenealogyEthnologySociologyHistoryLawEcology

Abstract

fetched live from OpenAlex

The introduction traces the main arguments of the book and provides an overview of key events discussed. It begins with the Sullivan Campaign of the American Revolution. This campaign ethnically cleansed Haudenosaunee people from territory that would later be ceded by the British to the Americans after the Revolution. The chapter asks what this campaign can tell us about the larger history of Indigenous–settler relationships. Among other things, it takes the campaign as an example of the rejection of real and fictive kinship ties between Indigenous peoples and settlers that was, I argue, a necessary precursor to the creation of settler nations. At the same time, some prominent imperial policy makers would struggle, as the book to come will also argue, to maintain different conceptions of Indigenous–imperial kinship in an effort to create a manageable and moral colonialism. The introduction outlines the book’s methodology of using microcosmic analyses to illuminate a macroscopic process: the project of British settler colonialism as it sprawled across time and space during this critical period of the creation and consolidation of settler colonial states. It gives an overview of pertinent scholarship and describes the topics of chapters to come.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.231
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 teacher head, not a consensus.

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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