The Commodification of Living Beings in the Fur Trade: The Intersection of Cheap Raw Materials and Cheap Labor
Bibliographic record
Abstract
Abstract The eighteenth- and nineteenth-century fur trade in the United States and Canada that sent hundreds of thousands of furs to Europe and China relied on “Cheap Labor” and the abundance of “Cheap Raw Materials,” that is to say, living beings such as sea otter, land otter, beaver, and seals. Native American labor, procured by and paid through trade goods in a kind of “putting out” piece-rate system, was cheap partially because their lives were maintained/reproduced through traditional agricultural or hunting and gathering economies. The commodification of fur-bearing animals led to their sharp decline and in some cases near extinction. Cheap labor and cheap living beings interacted dynamically in unison to enable capital accumulation under mercantile capitalism. At the very end of the nineteenth century, fur farming as a petty capitalist enterprise became common in Canada and the United States, and more recently has expanded greatly in China.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".