Bibliographic record
Abstract
M s Host of languages encountered by early settlers and explorers of North America belong to Algonquian family. With exception of Iroquois of Upper St. Lawrence and Siouan-speaking groups of Carolinas, Atlantic seaboard was occupied by Algonquian tribes whose names have survived in popular mythology, such as Powhatans of Virginia, and in novels of James Fenimore Cooper, such as Delawares and (heavily fictionalized) Mohegans. From there-across three centuries and better part of a continent-the Algonquian domain stretches to Blackfoot and Cheyenne of northern Great Plains and buffalo-hunting scenes of a Catlin or Bodmer.1 In Canada, most widely spoken languages, then and now, are those of Cree and Ojibwa complexes, with names such as Saulteaux, Algonquin and Ottawa for subdivisions of latter and Montagnais, Swampy Cree and Plains Cree for former. The earliest records of Algonquian languages date from beginning of seventeenth century: writings of Marc Lescarbot (Paris 1609) in New France, of John Smith (Oxford 1612) and William Strachey in Virginia, and then annual reports of Jesuits which began to be published with Paul LeJeune's Relation for 1633. Strachey's material, collected in 1610 and 1611, is the earliest recording in quantity (that has survived) on any Algonquian language (Siebert 1975: 292); although it was not published until modern times, it is typical of seventeenth century in that it is an appendix to a geographical and
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".