Linking community-based monitoring to water policy: Perceptions of citizen scientists
Bibliographic record
Abstract
This paper examines the relationships between Community-Based Water Monitoring (CBM) and government-led water initiatives. Drawing on a cross-Canada survey of over one hundred organizations, we explore the reasons why communities undertake CBM, the monitoring protocols they follow, and the extent to which CBM program members feel their findings are incorporated into formal (i.e., government-led) decision-making processes. Our results indicate that despite following standardized and credible monitoring protocols, fewer than half of CBM organizations report that their data is being used to inform water policy at any level of government. Moreover, respondents report higher rates of cooperation and data-sharing between CBM organizations themselves than between CBM organizations and their respective governments. These findings are significant, because many governments continue to express support for CBM. We explore the barriers between CBM data collection and government policy, and suggest that structural barriers include lack of multi-year funding, inconsistent protocols, and poor communication. More broadly, we argue that the distinction between formal and informal programming is unclear, and that addressing known CBM challenges will rely on a change in perception: CBM cannot simply be a less expensive alternative to government-driven data collection.
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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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".