An Ethnohistorian in Rupert’s Land: Unfinished Conversations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it