Bibliographic record
Abstract
For two centuries (1670-1870), English, Scottish, and Canadian fur traders voyages the myriad waterways of Rupert’s Land, the vast territory charted to the Hudson’s Bay Company and later splintered among five Canadian provinces and four American states. The knowledge and support of northern Native peoples were critical to the newcomer’s survival and success. With acquaintance and alliance came intermarriage, and the unions of European traders and Native women generated thousands of descendants. Jennifer Brown’s Strangers in Blood is the first work to look systemically at these parents and their children. Brown focuses on Hudson's Bay Company officers and North West Company wintering partners and clerks – those whose relationships are best known from post journals, correspondence, accounts, and wills. The durability of such families varied greatly. Settlers, missionaries, European women, and sometimes the courts challenged fur trade marraiges. Some officers’ Scottish and Canadian relatives dismissed Native wives and “Indian” progeny as illegitimate. Trades who wooks these ties seriously were obliged to defend them, to leave wills recognizing their wives and children, and to secure their legal and scoial status – to prove that they were kin, not “strangers in blood.” Brown illustrates that the lives and identities of these children were shaped by factors far more complex than “blood.” Sons and daughters diverged along paths affected by gender. Some descendants became Métis nationhood under Louis Riel. Other rejected or were never offered that course – they passed into white or Indian communities or, in some instances, identified themselves (without prejudice) as “halfbreeds.” The fur trade did not coalesce into a single society. Rather, like Rupert’s Land, it splintered, and the historical consequences have been with us ever since.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".