Essays On Disaster Management Issues Related To Household Preparedness And Public Attention
Bibliographic record
Abstract
This dissertation contains an essay on the effects of earthquake exposure on household preparedness in the short and long term and two essays on the predictors of public attention to earthquakes around the world. In Chapter I, I use a difference-in-differences method to estimate the causal effects of the 2010 and 2011 Canterbury earthquakes on people’s preparedness in the short-term (one month after the second earthquake) and long-term (up to 25 months after the second earthquake). I find that people who experienced the earthquakes increase their preparedness by 0.67 standard deviations in the short term. This impact stays positive but declines to 0.42 standard deviations in the long term. In chapter II, I investigate whether people from Western countries pay more attention to earthquakes in Western countries. I use Google Trends data and examine the proportion of Google searches from the United States, the United Kingdom, Canada, Australia, and New Zealand for 610 earthquakes across the world over the period of 2006-2016. I find that people in these countries pay on average around 50 percent more attention to earthquakes in Western countries. My results are significant and consistent after controlling for geographical and social characteristics but becomes small and insignificant once I control for GDP per capita of the countries where the earthquake struck. There seems to be a developed country bias rather than a Western country bias. In the final chapter, I measure public attention – using the volume of Google searches – from 18 countries and investigate which factors predict public attention to earthquakes at international level. I focus on 372 earthquakes registered as disasters in The Emergency Events Database (EM-DAT) over the period 2004-2018. I find that people pay more attention to earthquakes in richer countries, in more democratic countries, and in countries with which they have more social and cultural similarities. I also find that social and cultural similarities predict more public attention from Western and Latin American countries and less public attention from Arab and Sub-Saharan African countries. While, the findings of the economic and political status of countries are universal and predict more public attention in all four groups of countries.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".