Using Serious Games to Facilitate Collaborative Water Management Planning Under Climate Extremes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it