Constructing and Enforcing the “Medicine Line”: A Comparative Analysis of Indian Policy on the North American Frontier
Bibliographic record
Abstract
The national self-images of the United States and Canada have been shaped, in part, by their contrasting histories and mythologies of westward expansion and nation-building. Those narratives are most distinct with regard to government policies toward aboriginal peoples on either side of the 49th parallel, what Indians called “the medicine line.” The purpose of this article is two fold: (1) to specify and develop a three-part conceptual framework (consisting of the Turnerian discourse, the Lipset Thesis, and Borderlands Studies) for examining the history of the North American frontier and (2) utilizing a wide range of scholarly literature, to apply that framework in a comparative analysis of national policies toward Indians and First Nations in the post–Civil War/post–Confederation period on the Great Plains and Prairies. Several explanatory factors for cross-national difference will be identified and examined, including variance in geography and geology; demography, demographic trends, and political pressures in each country; the types of national political institutions and their impact on policymaking; and the types of forces deployed in the West (the Mounties and the US Army).
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".