Linking community-based monitoring to water policy: Perceptions of citizen scientists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it