The Civilizing Mission, Women’s Labor, and the Mixed-Race Families of the Old Northwest
Bibliographic record
Abstract
Abstract Traders and the mixed-race population that engaged in the fur trade became one of the principal means by which the government, federal and territorial, could influence the largely Indigenous population northwest of Detroit. Missionaries, sent by the American Board of Commissioners for Foreign Missions (ABCFM) to educate the mixed-race children of the American fur company employees, worked to foster a sense of national identity among these nascent citizens who lived at the American Fur Company posts. Fur traders, often French Canadian or British immigrants, were recognized as settler-citizens. Similarly, the Indigenous and mixed-race wives and children of American Fur Company employees were also recognized as American citizens. Traders and missionaries understood that marriage, especially to Indigenous or mixed-race women, was a necessary condition for success because of the crucial domestic and linguistic labor Native women provided. At odds with goals of the ABCFM, the U.S. government was not concerned about the civilization of the Odawaag and Ojibweg but rather wanted to convert their homeland into public domain to sell to white settlers. With statehood looming and the settler population booming, Henry Schoolcraft pressured the Anishinaabeg to sell their lands and accept new territory west of the Mississippi.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".