Towards a Framework for Designing and Assessing Game-Based Approaches for Sustainable Water Governance
Bibliographic record
Abstract
Most of the literature on serious games and gamification calls for a shift from evaluating practices to using theories to assess them. While the former is necessary to justify using game-based approaches, the latter enables understanding “why” game-based approaches are beneficial (or not). Based on earlier review papers and the papers in this special issue of Water entitled “Understanding game-based approaches for improving sustainable water governance: the potential of serious games to solve water problems”, we show that game-based approaches in a water governance context are relatively diverse. In particular, the expected aims, targeted audience, and spatial and temporal scales are factors that differentiate game-based approaches. These factors also strongly influence the design of game-based approaches and the research developed to assess them. We developed a framework to guide and reflect on the design and assessment of game-based approaches, and we suggest opportunities for future research. In particular, we highlight the lack of game-based approaches that can support “society-driven” sustainable water governance.
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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 | 0.057 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".