Stop Disasters 2.0: Video Games as Tools for Disaster Risk Reduction
Bibliographic record
Abstract
Increasingly, international organisations (e.g. UNESCO, UNISDR), governments (e.g. Canada), and non-government organisations (NGOs) (e.g. Save the Children, Christian Aid) and researchers use video games to raise disaster and disaster risk reduction (DRR) awareness. Yet, there is a paucity of studies on these games in the disaster literature. This article presents a typology specifically designed to deconstruct both disaster awareness building and mainstream disaster orientated video games, identifying how games like Stop Disasters, Disaster Watch, Inside Haiti, Earthquake Response, Fallout and SimCity instil disaster awareness, portray hazards, vulnerabilities, capacities, disasters and DRR. The article also touches upon ideas of game content, player motivation, skill building and social interaction in the context of disaster themed video games. The findings suggest video games have the potential to be positive tools to reinforce messages surrounding DRR, though further research is necessary. This article sets an agenda for future research.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".