Emperor of the North: Sir George Simpson and the Remarkable Story of the Hudson's Bay Company, by James Raffan
Bibliographic record
Abstract
Sixty-five years ago, the great American historian Samuel Eliot Morison, in his fine biography of Christopher Columbus, made a perceptive comment about historians who write biographies of explorers.He wrote (1942:xv): "This book arose out of a desire to know exactly where Columbus sailed on his Four Voyages, and what sort of a seaman he was.No previous work on the Discoverer of America answers these questions in a manner to satisfy even an amateur seafarer.Most biographies of the Admiral might well be entitled 'Columbus to the Water's Edge.'"Morison, who was a sailor as well as a historian, was not satisfied with a landlubber's history, and steered his boat around the Caribbean, following Columbus' path to find out where and how the discoverer had made his first landfall.Much the same might be said about histories of the fur trade and of northern exploration in Canada.Few writers, what he did, offering only a broad absolution to many of Kelsall's fellow officials in the Conclusion: "And while we have questioned many of those purposes, we have no wish to deny the honourable intentions … of many of the state's agents" (p.274).These are minor quibbles and should not detract from Kulchyski and Tester's achievement.They have taken on a vast swath of northern history, immersed themselves in the available material, and emerged with a compelling account of how relations between a modern state and a hunting society were bungled with lasting consequences.Even the creation of Nunavut has been influenced, and not entirely to the good, by the legacy of the events that occurred between 1900 and 1970.Kiumajut should be read by political scientists, wildlife managers, government officials, historians, and perhaps most importantly, by Inuit interested in understanding the origins of their political situation today.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".