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Record W4401703046 · doi:10.1017/s1537781424000215

Autonomy Through Allotment: Political Strategies of the Ottawa Tribe in Indian Territory, 1870–1892

2024· article· en· W4401703046 on OpenAlexaboutno aff
David Dry

Bibliographic record

VenueThe Journal of the Gilded Age and Progressive Era · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsAllotmentTribePoliticsAutonomyPolitical scienceGeographyHistoryGenealogyLawEcologyBiology

Abstract

fetched live from OpenAlex

Abstract The late nineteenth-century policy of allotting tribal lands into individually owned tracts is appropriately interpreted as a destructive federal effort to expropriate Native land and eliminate tribal identities. The Ottawa Tribe in Indian Territory, however, had divergent objectives in supporting allotment. This article argues the Ottawa advocated for allotment and U.S. citizenship to escape intrusive federal control over their lands and resources. Although they embraced policies aimed at eliminating tribal existence, the Ottawa rejected the intentions behind those policies, and instead, they drew on long-established community attributes of mobility and interconnection with outsiders to resituate their nation within American society. By centering Ottawa perspectives, this article disrupts progressive narratives that denote the pursuit of U.S. citizenship as an effort to secure equal inclusion. It underscores U.S. citizenship and allotment as tools of settler colonial domination and demonstrates how the Ottawa subversively deployed U.S. citizenship and private property rights to combat colonial administration and maintain tribal sovereignty. Examining a policy often glossed over as invariably imposed on Native nations, this article underscores the necessity of analyzing Native community dynamics and political strategies to understand the implementation and impact of allotment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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 routes1
Has abstractyes

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