A storm is brewing: Antecedents of disaster preparation in risk prone locations
Bibliographic record
Abstract
Abstract Research Summary Research emphasizes the value of disaster preparation and the importance of experience in doing so, yet most companies fail to prepare. The antecedents of preparation are poorly understood, in part, because experience by itself only partly explains the story. To address these concerns, we developed two unique surveys: one from an international survey in 18 disaster‐prone countries and another from a U.S. survey in New York City and Miami. We find that organizational experience with natural disasters increases preparedness for future hazards. Also, organizational learning from other businesses and organiztions positively mediates this relationship. Managers are more willing to learn from others in locations characterized by high‐impact, low‐frequency disasters. In areas with low impact, high frequency disasters, managers more likely misjudge the severity of natural disasters. Managerial Summary Despite the increasing frequency and severity of floods, storms, wildfires and other natural hazards, why do some firms in disaster‐prone areas prepare while others do not? To investigate, we conducted two studies: an international survey in 18 disaster‐prone countries and a U.S. survey in New York City and Miami. In both surveys, managers are more likely to prepare when their companies experienced prior disasters. Managers operating in locations characterized by high‐impact, low‐frequency disasters are more willing to learn from others. In contrast, managers in areas characterized by low impact, high frequency disasters, are more likely to prepare alone. Since effective disaster preparation typically entails working with, and learning from others, those companies that choose a go‐it‐alone strategy may misjudge disaster risk.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".