Exploring the Use of Experiential Learning Methods to Increase CBRNe Awareness and Emergency Preparedness of Children
Bibliographic record
Abstract
In recent years, there has been an increase in chemical, biological, radiological, nuclear, and explosive incidents, often involving or specifically targeting children. These emerging threats pose a significant risk to the physical, psychological, and social wellbeing of children and can cause damaging effects on their development and growth. Children are more susceptible to the lethal effects of CBRNe agents and require increased protection, specialized intervention and medical countermeasures, and expert mental health support post-incident. The Hyogo Framework established a widely adopted international commitment to educating children about disasters, through which many nations have implemented disaster risk reduction education platforms focused on increasing their knowledge about potential hazards. However, few countries have begun to explore the benefits of introducing a comprehensive CBRNe awareness and preparedness curriculum to children. Studies have shown that experiential learning methods offer a highly engaging and immersive learning experience and increase educational outcomes. This work aims to explore the potential benefits of developing an interactive educational tool to introduce basic skills to prepare children and communities against CBRNe incidents.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".