Collaborative watershed-based decision-making: Understanding land use-related risk to drinking water sources
Bibliographic record
Abstract
This thesis is an investigation of the value of social learning as a process that facilitates collaborative ecosystem approaches to planning with the intent of reducing risk to human health. More specifically, it examines the value of social learning in a planning process designed to protect quality of drinking water sources. A case study approach was used to examine Ontario's Source Water Protection planning process. Research focused on two different Source Protection Committees; both groups were in their second year of the planning process. Data were collected through semi-structured interviews, participant observation, and government documents. Based on a conceptual framework developed from the literature, the analysis examines the watershed as a social-ecological system, and as it relates to the adaptive cycle (Gunderson and Holling, 2002) and processes of social learning. Open and axial coding techniques were used to identify patterns and themes in the data. Study findings suggest that social learning is associated with the development of group trust, and more flexible and adaptive decision-making approaches. These results indicate that social learning can promote effective land use decision-making processes in the interest of protecting source water quality. Recommendations provide suggestions for enhancing opportunities for social learning as part of a collaborative Source Water Protection decision-making process.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".