An Ethnohistorian in Rupertâs Land: Unfinished Conversations
Bibliographic record
Abstract
In 1670, the ancient homeland of the Cree and Ojibwe people of Hudson Bay became known to the English entrepreneurs of the Hudson’s Bay Company as Rupert’s Land, after the founder and absentee landlord, Prince Rupert. For four decades, Jennifer S. H. Brown has examined the complex relationships that developed among the newcomers and the Algonquian communities—who hosted and tolerated the fur traders—and later, the missionaries, anthropologists, and others who found their way into Indigenous lives and territories. The eighteen essays gathered in this book explore Brown’s investigations into the surprising range of interactions among Indigenous people and newcomers as they met or observed one another from a distance, and as they competed, compromised, and rejected or adapted to change.While diverse in their subject matter, the essays have thematic unity in their focus on the old HBC territory and its peoples from the 1600s to the present. More than an anthology, the chapters of An Ethnohistorian in Rupert’s Land provide examples of Brown’s exceptional skill in the close study of texts, including oral documents, images, artifacts, and other cultural expressions. The volume as a whole represents the scholarly evolution of one of the leading ethnohistorians in Canada and the United States.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".