Community-Based Monitoring as a strategy of Indigenous water governance
Bibliographic record
Abstract
Alterations in water have significant implications for Indigenous peoples due to complex interconnections between environment, health, livelihoods and cultural well being. Indigenous peoples often express frustration with the inability to protect their complex socio-cultural relationships to water, in contexts where colonial forms of governance shape water rights and access. Yet, in spite of jurisdictional constraints, communities continue to engage multiple decolonial strategies aimed at protecting the waters within their territories. This paper analyzes community-based monitoring as one Indigenous water governance strategy. Specifically, I examine a transboundary case study of the Indigenous Observation Network – a community-based water quality monitoring network of Canadian First Nations and Alaska Native Tribes, coordinated by the Yukon River Inter-Tribal Watershed Council – in the Yukon River Basin. Analysis of semi-structured interviews with water quality samplers from across the watershed and other program partners reveal that communities value the program as it provides trusted baseline water quality data. At the same time, improvements could be made to monitor additional parameters of local concern, increase the use of data in decision-making processes and improve the sustainability of program funding.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".