Experiential Learning through Role-Playing: Enhancing Stakeholder Collaboration in Water Safety Plans
Bibliographic record
Abstract
Improved water safety management, as addressed by the Sustainable Development Goals, can be aided by Water Safety Planning, a risk-assessment and risk-management approach introduced by the World Health Organization and implemented to date in 93 countries around the globe. Yet, this approach still encounters some challenges in practice, including that of securing collaboration among the broad range of stakeholders involved. This paper presents a role-playing game designed to foster stakeholder collaboration in Water Safety Plans (WSP). In this role-play, participants take on different stakeholders’ roles during a collective (team-based) decision-making process to improve water supply safety in a fictive town. The game is the result of a transdisciplinary initiative aimed at integrating knowledge across technical and governance aspects of WSPs into an active learning experience for water sector actors from diverse backgrounds. It exposes participants to the four phases of Kolb’s experiential learning cycle: concrete experience, reflective observation, conceptualization and active experimentation. This paper discusses potential impacts of the WSP role-play, including skills and knowledge development among participants, which can support cross-sectoral integration and dealing with complexity in decision-making. These are capacity assets strongly needed to address water safety management challenges in a sustainable way.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".