Navigating ambiguous waters: a relational approach to nested conflicts in the Katari River Basin, Bolivia
Bibliographic record
Abstract
The Katari River Basin, the most densely populated basin in Bolivia, discharges into Lake Titicaca, the world’s highest navigable lake and a crucial water resource in the Andes. Despite its significance, the basin suffers from severe water contamination because of anthropogenic activities. This pollution adversely affects water quality, distribution, and availability, exacerbating the region’s vulnerability to the impacts of climate change at high elevations. In response to these challenges, the Bolivian government established a multi-stakeholder platform. However, this platform reveals complex water conflict dynamics linked to ambiguity associated with different ways of knowing, framing, and coping with water pollution issues. This study examines how relational practices are linked to managing ambiguity and addressing nested water conflicts. Relational practices are communication-based practices by which the involved actors shape and develop mutual and shared sense-making relationships. Our findings reveal that current relational practices hinder their ability to collaboratively address ambiguities, leaving underlying water conflicts unresolved. Moreover, they indicate that ambiguity is managed by imposing a singular frame, reinforced by the significant power asymmetries within the multi-stakeholder platform, strengthening the dynamics of water conflicts. We conclude that dealing with ambiguity through high-quality relational practices could facilitate the recognition and resolution of water conflicts, potentially improving clarity, communication, and advancing collaborative problem-solving among stakeholders.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".