“Putting Suppliers on the Map:” Centering Upstream Voices in Water Funds Outreach
Bibliographic record
Abstract
Abstract As water funds and other watershed investment programs expand around the world, there is growing interest in designing equitable programs that provide both upstream and downstream benefits. While research demonstrates that diverse values underlie upstream participation, existing communication and outreach materials from non‐governmental organizations (NGOs), governments, development banks, and others tend to highlight the goals of downstream actors (e.g., improving water supply for cities), with little attention to upstream perspectives. We present a case study in response to this gap, where we collaborated with a water fund and a river users association in Colombia to co‐produce a website entitled “Putting Suppliers on the Map” in which interviews and photography illuminate the perspectives of upstream participants and the intermediary organization. The website offers multiple lessons for communication and environmental education in water funds by shifting focus to the motivations of upstream participants, including trust‐building among upstream and downstream participants via intermediary actors, and informing downstream water users of the essential role of these processes for program success. Analyzing the website testimonials, we show that the vast majority of participants were motivated not only by overlapping instrumental and relational values associated with conservation, but also by a variety of personal and community goals. We found that the largest barrier to participation over time was the need to build trust between the water fund and rural communities and to align water fund goals with participants' motivations. By making visible the motivations and challenges of upstream actors, the website reverses the standard direction of environmental education (in which high‐level actors or downstream groups educate upstream residents). In‐so‐doing, the website aims to help downstream actors envision more productive and equitable ways of interacting with upstream participants.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".