Indian Treaty-Making Policy in the United States and Canada, 1867-1877
Bibliographic record
Abstract
Indian Treaty-Making Policy in the United States and Canada, 1867–1877 is a comparison of United States and Canadian Indian policies with emphasis on the reasons these governments embarked on treaty-making ventures in the 1860s and 1870s, how they conducted those negotiations, and their results. Jill St. Germain challenges assertions made by the Canadian government in 1877 of the superiority and distinctiveness of Canada’s Indian policy compared to that of the United States. Indian treaties were the primary instruments of Indian relations in both British North America and the United States starting in the eighteenth century. At Medicine Lodge Creek in 1867 and at Fort Laramie in 1868, the United States concluded a series of important treaties with the Sioux, Cheyennes, Kiowas, and Comanches, while Canada negotiated the seven Numbered Treaties between 1871 and 1877 with the Crees, Ojibwas, and Blackfoot. St. Germain explores the common roots of Indian policy in the two nations and charts the divergences in the application of the reserve and “civilization” policies that both governments embedded in treaties as a way to address the “Indian problem” in the West. Though Canadian Indian policies are often cited as a model that the United States should have followed, St. Germain shows that these policies have sometimes been as dismal and fraught with misunderstanding as those enacted by the United States.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.041 | 0.012 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".