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 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 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".