WUI READY: A Serious Game for Promoting the Adoption of a Wildland-Urban Interface Fire Hazard Mitigation Methodology
Bibliographic record
Abstract
The rising frequency and intensity of wildfires, particularly in wildland-urban interface communities, pose significant risks to residents. To address this, research and government organizations have developed wildfire hazard mitigation strategies. However, effective implementation requires residents to have adequate knowledge about such strategies. Serious games (i.e., designed for learning and training, not just entertainment) offer a promising and effective approach to educating communities about natural hazard mitigation. This paper presents the preliminary results of a research project focused on developing and evaluating a web-based serious game designed to educate residents about wildfire hazards and associated mitigation strategies, thereby enhancing their preparedness for future wildfire events. Four learning objectives were derived from a Hazard Mitigation Methodology to create a gaming framework and storyboard with four modules, which in turn guided the development of a preliminary game prototype. The game evaluation method has also been successfully designed. These results are expected to provide directions for policymakers and authorities, supporting informed decisions on how to develop and implement such educational tools. This project represents pioneering work in disseminating hazard mitigation information through a digital, web-based game, offering a scalable method that could be adopted by other organizations addressing the increasing impact of climate change-related disasters. Ultimately, this project lays the groundwork for further research and development of more comprehensive educational solutions that leverage gamification to promote hazard preparedness across various disaster contexts.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".