Kipiyododan Nibi: We Protect the Water. A conversation on collective Water protection, Indigenous rights and relationality
Bibliographic record
Abstract
This article presents the experience of Dara Wawatie-Chabot, a member of Ikwe Ganawenindan Nibi - a collective of Indigenous women living on unceded Algonquin territory who are engaged in protecting Nibi (Water). It demonstrates the value of a respectful and dynamic relationship that emphasizes allyship and relationality in the collective effort to protect Nibi, which is part of Aki (Land), while affirming the inherent rights of Indigenous people and of future generations. To honor the oral tradition and Indigenous pedagogies, this article takes the form of a conversation between Dara Wawatie-Chabot, an Algonquin Anishinaabe political science student, researcher and organizer, from Kitigan Zibi Anishinabeg and Barriere Lake, Quebec, and Emmanuelle Larocque, a non-Indigenous social work professor and activist who takes the role of an active learner. Through their conversation, Dara offers insights on the intersections between Water protection, Indigenous rights, sovereignty and resurgence, and Emmanuelle gains a deeper appreciation for the role of Indigenous knowledge in ecological management and beyond. By challenging Eurocentric ontologies with the knowledge of local Indigenous people, the article demonstrates how colonization and ongoing systematic violence continues to impact Indigenous peoples, their ways of life, and all living beings (humans and non-humans).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".