Autonomy Through Allotment: Political Strategies of the Ottawa Tribe in Indian Territory, 1870–1892
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".