Bibliographic record
Abstract
his latest volume of the First Nations Language Readers continues the evolution of the series and crosses further boundaries in a number of ways.Once again, we are able to present the very first collection of stories to ever see print in a particular Indigenous language of the Americas.I deliberately refer to Aaniiih/Gros Ventre in this way to highlight one obvious boundary that is being crossed.All previous volumes in this series have originated in Canada, while the people popularly known as the "Gros Ventre" are currently located solely within the United States.But such borders are recent and had no meaning for First Nations through the millennia of life here on Turtle Island, and traditional territories were not so restricted.In fact, the earliest recorded European contact with the Aaniiih took place in what is now Saskatchewan.I like to think we are helping the language to come home and be heard here once again.And if we are to hear the language again, we need also to recognize the true name of the speakers.Thus, we also hope to redress a misnomer that has persisted among English and French speakers since that early contact when the Aaniiih or "White Clay" people were erroneously referred to as the Gros Ventre or "big bellies."Though we have retained that name here to help identify the people and language to the wider audience, we have also insisted on including a respectful and appropriate
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.412 | 0.298 |
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".