Fox Trading and the Problem of Polar Bears in the Hudson’s Bay Company: Arctic Human Ecology and Fur in a Global Value Chain, 1900–1940
Bibliographic record
Abstract
Abstract Just before World War I, the Hudson’s Bay Company (HBC) geographically expanded its trade in the Canadian Arctic to derive profits from Arctic fox fur and secure its position in a global value chain (GVC) delivering fur to metropolitan consumers. The “problem of nature” challenged the company’s business venture. Furthermore, “nature” was made and remade by the HBC’s own capital investments. The fox trade itself changed human ecology. Technology transfers to Inuit modified their hunting regimes to increase the company’s returns of polar bear skins. Though these skins had high potential market value, modes of production introduced by the HBC to the Arctic precluded the company from sending high-quality products to metropolitan dressers. Within a changing Arctic human ecology, the HBC produced one highly valued commodity for the market while producing another from which it could derive only modest profit. The HBC’s fox and polar bear trade at the onset of the last century suggests ways that business empires can set off complex and unanticipated changes in human ecologies and, therefore, the dynamics of nature and business at their very peripheries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".