Land-based Learning as a Methodology for Understanding Indigenous Water Governance
Bibliographic record
Abstract
This chapter highlights the role Land-based learning can play in Indigenous community-led water governance. Indigenous community-led water governance has been highlighted as key to solving the current water crises within Indigenous communities (Hurlbert, 2022). Additionally, connection to Land 1 and culture has been identified as pillars of achieving Indigenous-led water governance. Given this, Indigenous Land-based learning has significant implications for Indigenous communities’ resource management, including water governance, particularly in Canada (Mowatt et al., 2020). Centring Indigenous community-led water governance in the broader context of Indigenous peoples’ sovereignty and self-governance, the intersection between Land and water is examined, including what this means for Indigenous-led water governance. Using Indigenous Land-based learning as a methodological framework, this chapter explores Indigenous Land-based learning as a theoretical lens for understanding Indigenous-led water governance. Indigenous Land-based learning sustains and promotes Indigenous governance (Wildcat et al., 2014). To Indigenous people, water is medicine and not just a resource for humans (Native Women’s Association of Canada, 2024). Understanding the relationship between Land and water is key to ensuring Indigenous water rights; this chapter aims to enhance access to safe drinking water by promoting Indigenous water sovereignty and self-governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".