Effective Communication of Natural Hazards in the Era of Information Explosion
Bibliographic record
Abstract
The Squamish-Lillooet Regional District (SLRD) in southwestern BC is prone to various natural hazards due to its unique geological and meteorological conditions. In response, local governments, NGOs, and academic institutions have implemented programs to mitigate these risks. However, the effectiveness of these programs and their alignment with community needs remain uncertain due to limited research. To address this knowledge gap, the Centre for Natural Hazards Research at SFU conducted an online survey from November 2021 to March 2022. The survey had three objectives: 1) establish a baseline understanding of the community's knowledge and preparedness for natural hazards, 2) identify factors influencing community behaviour, and 3) assess the effectiveness of communication methods. Our results indicate that while SLRD residents have a general awareness and reasonable preparedness, there is room for improvement. More than half of participants expressed dissatisfaction with current risk communication approaches and mitigation plans. Challenges in comprehending government-provided emergency maps were also identified. The findings from this survey have been compiled in this report, which provides valuable guidance for governments in natural hazard management and education.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".