Harmonizing Water Resource Management with Indigenous Ways of Knowing
Bibliographic record
Abstract
Increases in the global population and accompanying demands for water and food production are having detrimental impacts on the sustainability of freshwater systems. These impacts include reduced water quality, abnormal flow fluctuations, and changes in sediment transport by water, among others. Another stressor on watersheds is climate change, as it is for all sensitive ecosystems. The Saskatchewan River Delta (SRD) is no exception. Populations in the SRD, such as the Indigenous communities in Cumberland House, have been adversely affected by upstream water withdrawals for irrigation, dam-induced alterations of the seasonal river flows for hydropower, and legacies of industrial pollution. Although research has demonstrated these and other problems, to date the perspective of the Cumberland House community has been inadequately considered in water resources modeling efforts and flow management. Consequently, the residents of the Delta have seen little in the way of adaptations and solutions. \nIn this project, I sought to inform water resources and environmental modeling processes and practitioners with the values, insights, and perspectives of how altered water resource management in the SRD have changed from the point of view of the people of Cumberland House, so that developing models representing the Delta may better reflect local contextual factors in their execution. To achieve this objective, I used on-land participant observations and semi-structured interviews as a decolonizing tool to co-gather and analyze community members’ narratives on the issues in their environments. The results of this research identified and consolidated how the altered flows are affecting the Saskatchewan River Delta’s ecosystem and resident human and animal populations in terms of seasonality, livelihood, spiritual and cultural practices, and aesthetics. This research was completed within a community-engaged scholarship (CES) framework, which brought attention to issues in SRD communities, enhanced voice and agency of SRD residents, and paved the way for future knowledge incorporation not only in the SRD but also in other parts of the world, where interdisciplinary approaches to environmental sciences could lead to more vibrant and sustainable ecosystems.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.062 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".