A Line of Blood and Dirt: Creating the Canada–United States Border across Indigenous Lands
Bibliographic record
Abstract
In this thorough analysis, Benjamin Hoy joins a small but growing number of historians who have accepted the challenge of writing comparative studies of U.S. and Canadian issues. He has expanded the difficulty such efforts encounter by adding a third component: the Indigenous people in each country. In doing so he strides boldly into the intimidating linguistic swamp scholars must traverse when they study Indigenous topics. Questions such as what terms should be used for group names, whether bands, tribes, nations, or another term, must be addressed. Hoy explains his choices carefully. His central theme is that the creation of the Canada-U.S. border was difficult, messy, and mostly unplanned. Unlike international boundaries drawn after wars, or other territorial cessions, this line grew in fits and starts from the 1783 Treaty of Paris granting the United States independence to the 1846 Oregon treaty extending the line west along the 49th parallel. The analysis examines border making in five regions, the Atlantic Northeast, the St. Lawrence River valley, the Great Lakes area, the plains and prairies region, and the Pacific Northwest—giving most attention to the last three areas. Throughout the discussion, Hoy shows that while each nation wanted a strong border, neither had the money or the determination to create one effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".