Household Preparedness Typology and Coping Capacity: Implications for Building Disaster Resilience in Developing Countries
Bibliographic record
Abstract
This article examines flood preparedness characteristics and coping capacity of households based on the findings of two research studies conducted in Thailand, and discusses the implications for disaster resilience building. The first study looked at the characteristics of household preparedness. Data were collected using a questionnaire from 1,592 randomly selected households in Thailand's four regions, and descriptive statistics were obtained to analyze household preparedness characteristics. The first study's findings revealed four types of action, which were used to create a typology of household preparedness as a tool for analyzing the cost and amount of effort associated with each specific preparedness action, which, in turn, influences households' decision to adopt. The flood coping capacity of households was investigated in the second study. Data were collected using a survey questionnaire with 300 households in three flood-prone communities of Songkhla Province in Thailand's southern region, using a quota sampling method. Data were then analyzed using multiple regression technique. Both preparedness and human capital increased the level of household coping capacity, according to the findings. Based on the findings of these two studies, recommendations for improving people's resilience in disaster-prone communities in developing countries are proposed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".