Water’s for Fightin’: Lessons on Water Management Learned through Serious Gaming
Bibliographic record
Abstract
Canada is a water-rich country compared to other parts of the world, but its water resources are affected by climate change. One of the driest regions, the Palliser Triangle portion of the Canadian Prairies, is prone to drought, a problem further exacerbated by increasing demand for water over time due to growing populations and industries in the Saskatchewan–Nelson River basin. A serious game (SG) is developed that considers the complex water management challenges faced by the region, the first-in-time-first-in-right (FITFIR) system in Canada (similar to prior appropriation in the United States), and the benefits of water-sharing agreements in the Saskatchewan River basin. Results from the open-source SG are based on seven iterations played between 2014 and 2024 in undergraduate classes at two Canadian universities, facilitated by the same instructor. Feedback overwhelmingly supports the continued use of SG for water management learning and demonstrates the effectiveness of SG as a tool to support engagement around water management principles, resulting in participants feeling better equipped to consider the socioeconomic consequences of water scarcity in management decisions. The game highlights the challenges and benefits of implementing transboundary water share agreements and the limitations of FITFIR licensure in a drought-prone region. While initial implementation of the SG was in a classroom setting at universities in Canada, broader implementation and use by the water resources community presents the opportunity for citizen and stakeholder engagement around water management decisions and emerging supply–demand challenges.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".