Bibliographic record
Abstract
By the late Middle Ages, populations of fur-bearing animals had been heavily depleted across Europe. Conferring wealth and privilege, furs were symbols of superiority for the nobility and success for the upwardly mobile. Elites spent lavishly on furs, which sent merchants and monarchs alike in search of new sources. The most sought after fur-bearing animal was the beaver, whose winter coat was soft, warm, and could be easily felted. In search of beavers, some fur traders, including those from the English Muscovy Company (c. 1555) looked east, primarily to Russia. Others looked west to the New World. As a result, the pursuit of fur skins became an important driver of Atlantic expansion. The search for furs shaped the contours of European and Native American cultural contact and exchange and played a central role in the Atlantic contest for empire. Long before European contact, Native Americans valued fur skins for clothing, art, and as spiritual symbols. Traded among Native nations, furs later became important objects of exchange with Europeans. By the 17th century, Native hunters and French explorers had scoured waterways from Newfoundland to the St. Lawrence River and west to the Great Lakes. The English and their Indian allies hunted Hudson Bay to the north as well as the rivers of New England and the mid-Atlantic to the south. The Dutch and their Native partners scoured the region between the Delaware and Connecticut Rivers but were particularly attentive to the Hudson and Mohawk River watersheds—that is, until the English takeover in 1664 and again (and for good) a decade later. In search of furs, the Spanish and French pushed north from New Orleans into the plains, mountains, and deserts of the continental West. During the 18th and 19th centuries increasing numbers of Europeans and Euro-Americans ventured across continental North America on foot and by oar and paddle. Others crossed via the Panama isthmus or sailed around Cape Horn into the Pacific in search of furs. In some cases, they traded with Natives, and in others they trapped. They established corporations, which organized new economies of extraction, decimating populations of fur-bearing animals from the Missouri River to the Bering Strait. This reshaped river, prairie, mountain, and coastal ecologies across North America, which transformed European and Native American cultures and economies in return.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.030 |
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".