The Roles of Companion Animals in the Relationship Between Disaster Risk Perception and Willingness to Evacuate
Bibliographic record
Abstract
Abstract Companion animals are becoming increasingly common, and as natural hazards grow in frequency and severity, they play a critical role in guardians’ decision making about evacuation and shelter during disasters. Although many studies have explored the relationship between risk perception and willingness to evacuate, it remains unclear whether companion animals play a role in this relationship. This study investigated whether companion animal guardians exhibit a distinct risk perception-willingness to evacuate relationship compared to non-guardians during Category 1–2 and Category 3+ hurricanes. It also explored how guardianship characteristics, such as the number of animals or their dual role as support animals, influence this relationship. The findings indicate that being a guardian and the number of animals significantly affect willingness to evacuate and its connection to risk perception. For Category 3+ hurricanes, the presence of chronically ill animals further influences this relationship. Probability plots reveal that guardians have similar evacuation willingness as non-guardians at lower levels of perceived risk, but at higher levels of perceived risk, guardians show a significantly greater willingness to evacuate. Additionally, guardians with more animals are more likely to evacuate at a lower perceived risk but less likely at a higher perceived risk. For Category 3+ hurricanes, guardians of healthy animals show a higher evacuation willingness at lower levels of perceived risk than those with sick animals. These findings highlight the complex nonlinear role that companion animals play in evacuation decisions and provide insights into some of the contradictory evacuation behaviors by guardians reported in the literature.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".