K<scp>eith</scp> R<scp>ichotte</scp> J<scp>r</scp>. <i>Claiming Turtle Mountain’s Constitution: The History, Legacy, and Future of a Tribal Nation’s Founding Documents</i>.
Bibliographic record
Abstract
Scholarly discussions of tribal constitutions invariably revolve around the 1934 Indian Reorganization Act (IRA), a monumental piece of legislation that shaped modern tribal governments and triggered a flood of tribal constitution writing across Indian Country. However, the Turtle Mountain Tribe of north-central North Dakota adopted their first tribal constitution in 1932, two years before Congress passed the IRA. To explain Turtle Mountain’s jumpstart and, more importantly, to clarify the tribe’s complex relationship with its various governing documents, Keith Richotte Jr. delves deeply into the colonial history of the Plains Ojibwa and Métis peoples who call the reservation home. The misnamed Turtle Mountains are actually a collection of hills, plateaus, and wetlands along the North Dakota–Manitoba border. While most Anishinaabe peoples originally lived around the Great Lakes, by the eighteenth century a western group, known in English as the Plains Ojibwa, had moved into the Turtle Mountains. During the early nineteenth century, Métis people descended primarily from French traders, and Native women also found a home in the region. As both groups faced disappearing game and restricted movements across a hardening U.S.-Canadian border, their various strategies for resisting and coping with colonial forces crumbled.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.177 | 0.086 |
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".