Towards Participatory River Governance Through Citizen Science
Bibliographic record
Abstract
The concept of a “water governance crisis” manifests distinctly across different regions. In the Global South, particularly in rapidly urbanizing cities, innovative governance models that incorporate community participation are critically needed to address unique challenges such as informal settlements and less stringent pollution controls. This paper presents a theoretical and methodological approach, emphasizing citizen science and community engagement in urban water management. It explores how engaging communities in the assessment and management of water bodies not only enhances the identification of priority areas but also strengthens local capacities to address environmental challenges. An analytical framework highlighting the interdependence between valuation languages and citizen science supports the development of management models for degraded hydro-social territories. Utilizing a mixed-methods approach, this research develops social indicators and applies participatory methodologies, such as Participatory Mapping, demonstrated through a study of four urban rivers in Sangolquí, Ecuador: Santa Clara, San Pedro, Pita, and San Nicolás. Our findings reveal that participatory models are more effective than traditional technocratic hierarchies and underscore a new paradigm for water governance that prioritizes local knowledge and community practices. This study not only reveals the ecological, social, and spatial configurations of urban river landscapes in Sangolquí but also suggests the framework’s applicability to other Latin American cities facing similar challenges.
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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 | 0.063 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".