Honour water: Gameplay as a pathway to Anishinaabeg water teachings
Bibliographic record
Abstract
Digital games can uniquely express Indigenous teachings by merging design, code, art, and sound. Inspired by Anishinaabe grandmothers leading ceremonial walks known as Nibi Walks, Honour Water (http://www.honourwater.com/) is a singing game that aims to bring awareness to threats to the waters and offer pathways to healing through song. The game was developed with game company Pinnguaq and welcomes people from all over to sing with good intentions for the waters. The hope is to pass on songs through gameplay that encourages comfort with singing and learning Anishinaabemowin. Songs were gifted by Sharon M. Day and the Oshkii Giizhik Singers. Sharon M. Day, who is Bois Forte Band of Ojibwe and one of the founders of the Indigenous Peoples Task Force, has been a leading voice using singing to revitalize the waters. The Oshkii Giizhik Singers, a community of Anishinaabekwe who gather at Fond du Lac reservation, contribute to the healing for singers, communities, and the waters. Water teachings are infused in art and writing by Anishinaabe and Metis game designer, artist, and writer Elizabeth LaPensee. From development to distribution, Honour Water draws on Indigenous ways of knowing to reinforce Anishinaabeg teachings with hope for healing the water.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".