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Tsunami survivors' perspectives on vulnerability and vulnerability reduction: evidence from Koh Phi Phi Don and Khao Lak, Thailand

2010· article· en· W2089039415 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDisasters · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of WaterlooWestern University
Fundersnot available
KeywordsVulnerability (computing)GeographyEnvironmental healthDisaster risk reductionSocioeconomicsEnvironmental protectionMedicineEnvironmental planningSociologyComputer security

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.309
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it