Promoting Inclusive Institutional Culture Through Intergenerational Collaboration in Disaster Risk Reduction and Disaster Risk Management
Bibliographic record
Abstract
Disasters are becoming increasingly complex; disaster prevention, mitigation, response, and recovery must continue to transform to meet that complexity. Global guidelines stipulate using an “all-of-society” approach to disaster risk reduction (DRR) and disaster risk management (DRM) as a strategy to include those disproportionately negatively affected by disasters (e.g., youth). Youth participation in DRR and DRM is one way to apply this approach, but implementation of it can be challenging. As the COVID-19 pandemic and other disasters have shown, youth are volunteering and working before, during, and after disasters. In this study, we interviewed 12 youth between 12 and 24 years of age who have experience volunteering or working in local disasters in Ottawa, Ontario, Canada, to explore perceived barriers and facilitators to meaningful youth participation in DRR and DRM. We used reflexive thematic analysis to analyze the interviews and found that institutional and age-based discrimination are central barriers to effective youth participation. Intergenerational collaboration is integral to dismantling systemic social barriers, like ageism, to implement meaningful youth participation and promote inclusive institutional cultures across disciplines. Provided youth-specific supports such as strong communication, mentorship, training, advocacy, and recognition, intergenerational collaboration can create sustainable opportunities to integrate youth in DRR decision-making and DRM activities. These strategies support community resilience and adaptive capacity for future disasters, including pandemics.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".