Using Serious Games to Facilitate Collaborative Water Management Planning Under Climate Extremes
Bibliographic record
Abstract
Abstract Sustainable management is a complex process that involves balancing the competing interests of the human, plant, and animal communities that depend on watershed resources. It involves developing and implementing plans, programs, and projects that sustain and enhance watershed functions while taking into account the natural, social, political, economic, and institutional factors operating within the watershed and other relevant regions. Examples of such factors include crosscutting mandates by different levels of government, conflicting objectives across sectors, and the constraints and uncertainty of the availability and accessibility of the resources within the watershed. One way to address these complexities is with public participation processes designed to share knowledge among disciplinary experts, policy‐makers, and local stakeholders and provide outcomes, which inform the creation of sustainable watershed management plans. Serious games (i.e., games played for purposes other than pure entertainment) are an example of such processes. Here, we present a case study of how a serious game, called the multi‐hazard tournament, was used to facilitate watershed management by promoting social learning, cross‐sectoral dialogue, and stakeholder participation in the planning process.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".