Engaging stakeholders across a socio-environmentally diverse network of water research sites in North and South America
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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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".