Indigenous Women’s Knowledge: Prairies, Power, and Plants
Bibliographic record
Abstract
Although many people think that bison and men hunting bison were central to the story of Indigenous peoples on the northern Great Plains, Rosalyn LaPier argues that plants and women harvesting plants were essential to Indigenous peoples livelihoods, their ecological understanding, and religious practice. She will explore the world of Amskapi Piikani or Blackfeet women and those who identified with womanhood from the 19th century to the present on the prairies of North America. In fall 2022, LaPier joined the faculty of the Department of History at the University of Illinois, Urbana-Champaign; she also holds an appointment as a Research Associate at the National Museum of Natural History, Smithsonian Institution. Her work is within Indigenous communities to revitalize Indigenous and traditional ecological knowledge (TEK), to address environmental justice & the climate crisis, and to strengthen public policy for Indigenous languages. Dr. LaPier is an enrolled member of the Blackfeet Tribe of Montana and Métis. She is one of eight Indigenous professors nationally in an Environmental Studies/Sciences/Sustainability department and the first and only Blackfeet tribal member to be in a tenure-track position and to receive tenure from the University of Montana. LaPier also co-founded Saokio Heritage, a community-based organization which is led by Indigenous women and serves as a platform to amplify Indigenous women activists and writers. She is currently working on her third book, tentatively titled, Plants that Purify: Essays on the Ecology of Blackfeet Womanhood. LaPier’s book, Invisible Reality: Storytellers, Storytakers, and the Supernatural World of the Blackfeet won the 2018 John C. Ewers Book Award for best book on ethnohistory of North America and the 2018 Donald Fixico Award for best book on American Indian and Canadian First Nations History.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".