Engaging stakeholders across a socio-environmentally diverse network of water research sites in North and South America
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
Maintaining and restoring freshwater ecosystem services in the face of local and global change requires adaptive research that effectively engages stakeholders. However, there is a lack of understanding and consensus in the research community regarding where, when, and which stakeholders should be engaged and what kind of researcher should do the engaging (e.g., physical, ecological, or social scientists). This paper explores stakeholder engagement across a developing network of aquatic research sites in North and South America with wide ranging cultural norms, social values, resource management paradigms, and eco-physical conditions. With seven sites in six countries, we found different degrees of engagement were explained by differences in the interests of the stakeholders given the history and perceived urgency of water resource problems as well as differences in the capacities of the site teams to effectively engage given their expertise and resources. We categorized engagement activities and applied Hurlbert and Gupta's split ladder of participation to better understand site differences and distill lessons learned for planning comparative socio-hydrological research and systematic evaluations of the effectiveness of stakeholder engagement approaches. We recommend research networks practice deliberate engagement of stakeholders that adaptively accounts for variations and changes in local socio-hydrologic conditions. This, in turn, requires further efforts to foster the development of well-integrated research teams that attract and retain researchers from multiple social science disciplines and enable training on effective engagement strategies for diverse conditions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it