What Participation? Distinguishing Water Monitoring Programs in Mining Regions Based on Community Participation
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
Water issues are a major concern for the mining sector and for communities living near mining operations. Water-related conflicts can damage a firm’s social license to operate while violent conflicts pose devastating impacts on community well-being. Collaborative approaches to water management are gaining attention as a proactive solution to prevent conflict. One manifestation of these efforts is participatory water monitoring (PWM). PWM programs have the potential to generate new scientific information on water quantity and quality, improve scientific literacy, generate trust among stakeholders, improve water resource management and ultimately mitigate conflict. The emergence of PWM programs signals a shift toward greater stakeholder collaboration and more inclusive water governance within mining regions. In this article, we propose a new framework to evaluate the degree and extent of community involvement in PWM programs. This framework builds on citizen science literature. When applied to 20 cases in Latin America, notable differences in the degree of community and company participation between PWM programs are found. These differences suggest that companies and communities approach these programs from very different points of view. It is concluded that more attentive collaboration between firms and communities in the design of the program, the collection of data and interpretation of the results is needed to effectively build trust through PWM.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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