Understanding Game-based Approaches for Improving Sustainable Water Governance
Bibliographic record
Abstract
The sustainable governance of water resources relies on processes of multi-stakeholder collaborations and interactions that facilitate knowledge co-creation and social learning. Governance systems are often fragmented, forming a barrier to adequately addressing the myriad of challenges affecting water resources, including climate change, increased urbanized populations, and pollution. Transitions towards sustainable water governance will likely require innovative learning partnerships between public, private, and civil society stakeholders. It is essential that such partnerships involve vertical and horizontal communication of ideas and knowledge, and an enabling and democratic environment characterized by informal and open discourse. There is increasing interest in learning-based transitions. Thus far, much scholarly thinking and, to a lesser degree, empirical research has gone into understanding the potential impact of social learning on multi-stakeholder settings. The question of whether such learning can be supported by forms of serious gaming has hardly been asked. This Special Issue critically explores the potential of serious games to support multi-stakeholder social learning and collaborations in the context of water governance. Serious games may involve simulations of real-world events and processes and are challenge players to solve contemporary societal problems; they, therefore, have a purpose beyond entertainment. They offer a largely untapped potential to support social learning and collaboration by facilitating access to and the exchange of knowledge and information, enhancing stakeholder interactions, empowering a wider audience to participate in decision making, and providing opportunities to test and analyze the outcomes of policies and management solutions. Little is known about how game-based approaches can be used in the context of collaborative water governance to maximize their potential for social learning. While several studies have reported examples of serious games, there is comparably less research about how to assess the impacts of serious games on social learning and transformative change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".