Cherry River: <i>Art, Music, and Indigenous Stakeholders of Water Advocacy in Montana</i>
Bibliographic record
Abstract
The 2018 performance event, Cherry River, Where the Rivers Mix, designed by Indigenous scholar, Dr. Shane Doyle, a member of the Apsaalooke Crow community, in collaboration with artist Mary Ellen Strom, a founder of the nonprofit Mountain Time Arts program, endeavored to address the surging environmental problems associated with water in Montana. Drought and water scarcity impacts a diverse population, including Indigenous communities and the life of nonhuman plant and animals beyond the urban and rural populace of landowners, ranchers, and farmers. In 2021, the U.S. Department of Agriculture declared a federal emergency of drought disaster in a majority of Montana’s counties, and the recent disappearance of glaciers at Glacier National Park is of great concern. Doyle and Strom sought the opportunity to foster relationships and greater dialogue among regional constituencies, particularly among Indigenous and non-Indigenous communities, and they were successful in raising awareness regarding the need for equitable water use and conservation. Cherry River brought an audience of local people to the banks of the Missouri River Headwaters, where the Gallatin, the Jefferson, and the Madison rivers converge to present a mix of American music—Crow and Northern Cree singing, Métis violin, Big Band Jazz. The sound of the music of the rivers, however, was the all-encompassing engagement for those who attended. Drought and environmental crisis impels us to think more broadly about the role of the arts and humanities in environmental studies. Can the arts and performance contribute a different model for environmental advocacy, acknowledge a different perspective for understanding ecologies, and therefore expand the transdisciplinary process for engaging in environmental studies?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".