Bibliographic record
Abstract
A lthough the word indian as applied to the aboriginal inhabitants of North America has fallen into disfavour, so that some writers now prefer Amerindian, I chosen to continue the more common usage, if only Plains Indian is employed somewhat casually to describe all the buffalo-hunting who ranged the Great Plains.Because it originated as pejorative, and also because it is seen as too embracing, the Sioux also carries disapproval, Dakota being the acceptable term.There are main linguistic divisions, Dakota, Nakota and Lakota, spoken, respectively, by groups known as the Santee, Yankton and Teton, each group in turn consisting of numerous individual bands or tribes.The Tetons, the most westerly, alone divided into seven councils.Crazy Horse, for example, was of the Oglala and Sitting Bull was of Hunkpapa.Again, for simplicity, Sioux has been employed to refer to all.Similarly, the term Blackfoot is used collectively to refer to not only the Blackfoot people proper, but also those of the Blackfoot Confederacy, which included the Blood, the Sarcee, and the Peigan.As well, Ojibwa and Saulteaux refer to one common people.And no has been to distinguish between the Plains Cree, Woodland Cree or Swampy Cree.In the early years of the Red River Settlement, the terms Half-Breed and Métis carried separate definitions.Half-Breed designated mixed-blood descendants of Indian and Scottish or English parentage, while Métis referred
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.033 | 0.051 |
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".