Serious Games for Public Safety: How Gamified Education Can Teach Ontarians Emergency Preparedness
Bibliographic record
Abstract
According to the Canadian Emergencies act, a national emergency is an urgent, critical situation that threatens the health and safety of Canadians (Department of Justice of Canada, 2022). Emergencies can also take on many forms: pandemics, natural disasters, civil unrest, or armed conflict. Currently, the Provincial Emergency Response Plan implemented by the Chief of Emergency Management Ontario is the framework that keeps Ontarians safe, allowing for organizations and municipalities to organize disaster relief, send out emergency alerts, and educate Ontario residents on emergency preparedness (PERP, 2019). This paper explores how serious games can prepare the public for emergencies based on response frameworks currently in use in metropolitan Ontario, Canada (cities such as Toronto, Ottawa, and Hamilton). This example was selected because it represents modern urban settings that require response plans and provides a framework that can be used to elaborate on. This paper will present the positive features of serious game applications concerning public safety and emergency management education. Case studies of serious game applications currently used for public health and safety purposes will be examined. Serious games may be a useful instrument for public safety education to enhance existing emergency preparedness and public safety education frameworks.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".