Tsunami survivors' perspectives on vulnerability and vulnerability reduction: evidence from Koh Phi Phi Don and Khao Lak, Thailand
Bibliographic record
Abstract
This paper presents the results of primary research with 40 survivors of the 2004 Indian Ocean tsunami in two communities: Khao Lak (n=20) and Koh Phi Phi Don (n=20), Thailand. It traces tsunami survivors' perceptions of vulnerability, determines whether residents felt that the tsunami affected different communities differently, identifies the populations and sub-community groups that survivors distinguished as being more vulnerable than others, highlights community-generated ideas about vulnerability reduction, and pinpoints a range of additional vulnerability reduction actions. Tsunami survivors most consistently identified the 'most vulnerable' community sub-populations as women, children, the elderly, foreigners, and the poor. In Khao Lak, however, respondents added 'Burmese migrants' to this list, whereas in Koh Phi Phi Don, they added 'Thai Muslims'. Results suggest that the two case study communities, both small, coastal, tourism-dominated communities no more than 100 kilometres apart, have differing vulnerable sub-groups and environmental vulnerabilities, requiring different post-disaster vulnerability reduction efforts.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".