Ganawendan Ginibiiminaan (Take care of our Water!): mobilizing for Watersheds-at-risk with the Bad River Ojibwe
Bibliographic record
Abstract
Community-based research with the Bad River Band of Lake Superior Ojibwe in northern Wisconsin illustrated that Water stewardship is an organizing practice, value-laden, that brings together tribal and non-tribal people. Lead author Conaway collaborated over four years with tribal members to create a network of university, natural resource agency, and indigenous experts. Ojibwe co-author Leoso provided expertise in protocols and traditional knowledge. We worked in community Water stewardship from concept to the dissemination of durable products of which the tribe took ownership. This article focuses on methodology for outsiders working in Indian country, emphasizing indigenous research methods, and culminating in a case study of Water stewardship that incorporates Native and Western science. A local Anishinaabemowin version of traditional ecological knowledge (TEK) is highlighted: mino bimaadiziwin, “living in a good way.” Interviews with Bad River adults and Talking Circles with youth indicated that Ojibwe identity and value systems are bound to Water, shedding light on intangible dimensions of TEK. Water-based harvests, stewardship, sovereignty, and worldviews constitute an Ojibwe Water schema, Water TEK. We demonstrate that the vulnerability and resilience of Water and cultural traditions are intertwined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".