Territorial Expansionists and Troublesome Locals: Daniel Clark at New Orleans and John Christian Schultz at Red River
Bibliographic record
Abstract
Abstract The United States and Canada began major efforts to acquire territory in North America’s interior in the nineteenth century. In 1803, the United States purchased approximately 827,192 square miles of territory from France. Canada began to govern 1,409,900 square miles of territory known as Rupert’s Land in 1869. Historical accounts of American and Canadian officials’ efforts in Washington and Ottawa to obtain these vast territories are abundant. Historians have devoted less attention, however, to expansionists who supported American expansion in Louisiana and Canadian expansion in Rupert’s Land. Daniel Clark, U.S. Consul to New Orleans, used his office to promote American acquisition of Spanish Louisiana. Canadian John Christian Schultz, an influential “doctor” and businessperson at Red River, became the owner of the only newspaper in the settlement and used it to advocate for Canadian possession of Rupert’s Land. Clark arrived in Louisiana from Ireland in 1786 and Schultz from Upper Canada between 1859 and 1861. Clark and Schultz were opportunists who understood that American and Canadian territorial ambitions presented them with opportunities to advance their business and political careers. Far less important to both men were the futures of other residents of New Orleans and Red River. Clark and Schultz for example, both argued that representative local government should not be immediately introduced in their regions.
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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 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".