“I believe this team will change how society views youth in disasters”: the EnRiCH Youth Research Team: a youth-led community-based disaster risk reduction program in Ottawa, Canada
Bibliographic record
Abstract
SETTING: The Sendai Framework for Disaster Risk Reduction promotes an "all-of-society" approach to disaster risk reduction (DRR). Since 2013, the EnRiCH Research Lab has implemented a community-based, participatory program to promote youth development and engagement in DRR in Ottawa-Gatineau. The EnRiCH Youth Research Team used an existing community education program called the Enrichment Mini-Course Program as a framework to engage youth in DRR. We aim to share the implementation process and lessons learned from this innovative "all-of-society" approach to DRR. INTERVENTION: The EnRiCH Youth Research Team provides high school and university students with a platform to be heard on disaster and climate change issues. Youth are given opportunities to design and lead knowledge dissemination projects intended to educate members of the community about disaster prevention and preparedness. Students have opportunities to connect with academics, governmental and non-governmental organizations, and public health practitioners to share their ideas on youth participation in DRR in Canada. OUTCOMES: To date, this public health intervention has produced DRR training modules that can be used as curriculum support by teachers, a children's book on earthquake preparedness, an educational video about youth participation in DRR, and several conference presentations. Members of the team have become well versed in disaster preparedness strategies. IMPLICATIONS: This program has demonstrated that youth can contribute to DRR through knowledge mobilization, and support public education about disaster preparedness. Offering this opportunity at a grassroots level can support participation by youth by allowing flexibility in design and adaptation to individual environmental and social contexts.
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".