Bibliographic record
Abstract
“Great God! This is an awful place! ” were words used by Scott at the South Pole (Pound 1967), but they could have come just as well from the lips of a white woman on finding herself in the proximity of either Pole. Although some Viking woman undoubtedly preceded her onto the North American continent, Natalya Shelekhov became the first European female in Alaska when she and her merchant husband spent four years (1783-1787) on Kodiak Island. (He later founded the Russian-American Company.) Natalya’s visit did not precipitate a female invasion, however, and Russian merchants largely kept to native “wives ” in North America (Chevigny 1942). In Canada, at the beginning of the nineteenth century, Indian women likewise became the “country ” (common law) wives of fur traders and trappers of both the Hudson’s Bay Company and the North West Company; they were known as petticoat politicians because of their unofficial, though pronounced, influence on their mates ’ decisions. By the 1820’s men started bringing European women to Indian country, but by 1825 one, Captain R. P. Pelley, Governor of Red River,
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".