Bibliographic record
Abstract
sylvia van kirk has influenced the writing of American history as have few Canadian historians.Her influence is most evident in histories of the U.S. fur trade, women in the U.S. West, and in histories of Native-Newcomer relations.The frameworks of these fields shifted in the 1980s through Van Kirk's influence and that of other path-breaking scholars who placed American Indian women and other women of colour at the centres of history, and whose scholarship established the intertwined significance of race and gender as analytical categories.Sylvia Van Kirk first made her mark in the United States, as in Canada, in histories of the fur trade.The U.S. fur trade was smaller than the Canadian trade; it covered a shorter time span and occupied a less central place in narratives of U.S. development and westward expansion than has the fur trade in Canadian history.More than most U.S. scholars, American fur trade historians have been familiar with the work of their Canadian colleagues, and, in the 1970s and early 1980s, joined a rare cross-border cohort of fur trade scholars that included Arthur
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".