Citizen deliberation in the context of Uruguay's first National Water Plan
Bibliographic record
Abstract
Abstract As part of the formulation of the first National Water Plan (NWP) in Uruguay, a mini-public process called ‘Citizen Deliberation on Water (Deci Agua)’ was developed in 2016. While the draft of the plan was being discussed in the formal arenas of water governance (Basin Commissions and Regional Water Resources Councils), a University research team (led by the authors), in coordination with the national water authority, adapted the mechanism of consensus conferences in order to incorporate the citizens’ visions and to contribute to public understanding of the NWP challenges. This article analyses the main aspects of the developed participation strategy and discusses them regarding a set of quality criteria used to evaluate deliberative processes. Although the final version of the NWP (passed by decree in 2017) incorporated some of the contributions of the Citizen Panel, an in-depth analysis of the scope of the deliberative process of Deci Agua allows us to delve into some key aspects related to the quality of participation processes and the challenges. A mixed approach that combines stakeholder participation and lay citizens is novel and desirable in water governance since it increases the scope of participation, deepens the legitimacy of decision-making and improves the public debate.
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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 | 0.044 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".