Measuring people's preferences for cyclone vulnerability reduction measures in Bangladesh
Bibliographic record
Abstract
Purpose This paper aims to examine people's preferences for alternative cyclone vulnerability reduction measures in cyclone prone areas of Bangladesh. Design/methodology/approach A choice experiment (CE) method has been implemented based on the pressure and release (PAR) vulnerability model. Data were collected from a sample of households in two districts of Bangladesh in winter 2008. Findings The results of a choice experiment (CE) method conducted in selected areas of Bangladesh prone to cyclone hazards indicated that access to resources is viewed as the most influential factor in cyclone vulnerability reduction options. Findings support the pressure and release model (PAR) of vulnerability analysis. Access to training and education and cyclone warning systems are also found to have significant impacts on households' choices of cyclone vulnerability reduction. Structural mitigation measures and access to power and decision making, though significant, were found to have the least impact. Research limitations/implications The paper shows that the choice experiment method is a good technique for understanding people's preferences for vulnerability reduction measures. Practical implications The paper concludes with a policy recommendation for governmental and non‐governmental agencies to focus on vulnerability reduction measures that tackle the root causes of vulnerability. Originality/value This is the first time that the choice experiment method has been used for cyclone vulnerability analysis, and it provides quantitative supports for the PAR model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".