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Record W4363642962 · doi:10.1093/jahist/jaad009

A Line of Blood and Dirt: Creating the Canada–United States Border across Indigenous Lands

2023· article· en· W4363642962 on OpenAlexaboutno aff
Roger L. Nichols

Bibliographic record

VenueJournal of American History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsDirtIndigenousHistoryIndigenous culturePolitical scienceArchaeologyGeographyCartographyEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0400.008
Scholarly communication0.0100.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.001

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.

Opus teacher head0.009
GPT teacher head0.261
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of American HistorySame topicCanadian Identity and HistoryFrench-language works237,207