Beyond Physical: Impacts of Water Regulations in First Nations Communities
Bibliographic record
Abstract
In Standing Buffalo Dakota First Nation, there is increasing pressure on water resources by increased cottage development, sewage disposal to the river system, management of the Qu’Appelle and Gardiner Dams, impacting water flows and levels and increased flooding events. At the time this project was initiated, the community had serious concerns about the impacts of a proposal from a multinational potash mining company to withdraw water from Katepwa Lake for use in mining operations. The community was concerned with the impact on water quality, water level, and traditional and cultural activities pertaining to water. Initial meetings with Chief and Council also revealed that seasonal flooding threatens human safety, homes, and critical infrastructure, and the implementation of emergency measures puts considerable strain on the Band’s resources. Shortly after this research project began, the mining company withdrew their proposal to withdraw water. However, Standing Buffalo remained interested in exploring the significance of water to the community and the ways in which water (and the surrounding natural environment) is important and valuable to the community’s culture and traditions. Given the geographic location of the reserve, there are ongoing and potentially increasing impacts related to water that could arise from both anthropogenic and natural changes in the environment
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".