Communication Innovations for Adaptation to Climate Change and Increased Disaster Risk
Bibliographic record
Abstract
This study investigates the critical role of environmental communication in disaster management within Pangandaran Regency, Indonesia, emphasizing the enhancement of community engagement and preparedness in response to increasing disaster risks exacerbated by climate change.Utilizing a qualitative research design, in-depth interviews were conducted with 15 participants from various stakeholder groups, including local government representatives, village heads, community center leaders, and members of local disaster response organizations.Data collection methods included semistructured interviews, observations, and social media analysis, allowing for a comprehensive understanding of communication practices and challenges in disaster management.Key findings reveal the effectiveness of tailored communication strategies that resonate with local cultures, highlighting the importance of community involvement in disaster preparedness initiatives.Additionally, the study underscores the fundamental role of educational programs in fostering disaster resilience.It emphasizes the need for innovative communication approaches that cater to the region's unique socio-cultural dynamics and points to the potential benefits of community-based initiatives in enhancing disaster preparedness.The research contributes significantly to the field of disaster management by identifying specific communication barriers and recommending strategies for improvement.It advocates for using social media in disaster education, building partnerships with local organizations, and improving outreach to foster a culture of preparedness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".