Linking school based monitoring to land and water decision-making in the Nechako watershed
Bibliographic record
Abstract
Climate change is compounding existing threats to waters from land use activities such as forestry, agriculture, and mining, requiring alternative approaches to caring for watersheds. Community science and school-based monitoring are gaining attention as processes for communities and youth to become involved in decision-making by collecting data about the health of their lands and waters. However, due to the complexity of social-ecological systems, connecting community science to decision-making is a recognized challenge requiring more qualitative research that engages various actors. In response, this action-research project aimed to co-design water monitoring tools with students, teachers, and decision-makers to explore potential avenues for school-based monitoring to inform decision-making. The project focused on the case study of the “Koh-learning in our Watersheds” education initiative on Saik’uz First Nation Territory near Vanderhoof, British Columbia. Research activities took place with high-school classes from the Nechako Valley Secondary School at locations along Murray Creek, a tributary to the Nechako River. Phases of water monitoring actively shaped and informed qualitative research interviews and workshops to bring together youth, teachers, and decision-makers. Pathways identified for school-based monitoring to inform decision-making include: 1) increased attention on waterways, 2) identifying issues and imagining solutions, 3) filling gaps and providing new data, 4) behaviour change and stewardship, 5) contributing to reconciliation, and 6) conversations for action. The findings highlight that the strengths of school-based monitoring lie in its ability to contribute imaginative solutions to local problems that perplex decisionmakers and, when attuned to and aligned with Indigenous governance, can meaningfully support truth and reconciliation. Informed by these findings, an adapted version of the framework for a ‘social learning approach to monitoring’ is proposed and may serve as a tool in the design of future school-based monitoring that can target multiple pathways to influence decision-making. This research underscores that when connecting across knowledges, generations, and linking with decision-making, school-based monitoring can support a paradigm shift in water management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".