Measuring Household Resilience to Floods: a Case Study in the Vietnamese Mekong River Delta
Bibliographic record
Abstract
The flood is a well-known phenomenon in the Vietnamese Mekong River Delta (MRD). Although people have experienced the impact of floods for years, some adapt well, but others are vulnerable to floods. Resilience to floods is a useful concept to study the capacity of rural households to cope with, adapt to, and benefit from floods. Knowledge of the resilience of households to floods can help disaster risk managers to design policies for living with floods. Most researchers attempt to define the concept of resilience; very little research operationalizes it in the real context of "living with floods". We employ a subjective well-being approach to measure households' resilience to floods. Items that related to households' capacity to cope with, adapt to, and benefit from floods were developed using both a five-point Likert scale and dichotomous responses. A factor analysis using a standardized form of data was employed to identify underlying factors that explain different properties of households' resilience to floods. Three properties of households' resilience to floods were found: (1) households' confidence in securing food, income, health, and evacuation during floods and recovery after floods; (2) households' confidence in securing their homes not being affected by a large flood event such as the 2000 flood; (3) households' interests in learning and practicing new flood-based farming practices that are fully adapted to floods for improving household income during the flood season. The findings assist in designing adaptive measures to cope with future flooding in the MRD.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".