Reviewing volcano hazard and risk communications in Ecuador: experiences from a fast-format workshop
Bibliographic record
Abstract
Hazard and risk communication requires the design and dissemination of clear messages that enhance people’s actions before, during, and after volcanic crises. To create effective messages, the communication components such as message format and content, must be considered. Changes in technology are changing the way people communicate at an ever-increasing pace; thus, we propose revising the basic components of the communication process to improve the dialogue between scientists and the public. We describe communication issues during and outside volcanic crises in Ecuador and assess possible causes and consequences. These ideas were discussed during the short-duration “Volcano Geophysical Principles and Hazards Communications” Workshop in Baños, Ecuador in 2019. We review and propose communication strategies for volcanic hazards and risks that resulted from the workshop discussions and experiences of experts from the Instituto Geofísico (IG-EPN), local and international professors involved in volcano research and communication, and students from universities across Ecuador.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".